wrrg

merge fix

dashboard/package-lock.json

@@ -863,7 +863,6 @@
 			"integrity": "sha512-oH8tXw7EZnie8FdOWYrF7Yn4IKrqTFHhXvl8YxXxbKwTMcD/5NNCryUSEXRk2ZR4ojnub0P8rNrsVGHXWqIDtA==",
 			"dev": true,
 			"license": "MIT",
-			"peer": true,
 			"dependencies": {
 				"@standard-schema/spec": "^1.0.0",
 				"@sveltejs/acorn-typescript": "^1.0.5",
@@ -903,7 +902,6 @@
 			"integrity": "sha512-Y1Cs7hhTc+a5E9Va/xwKlAJoariQyHY+5zBgCZg4PFWNYQ1nMN9sjK1zhw1gK69DuqVP++sht/1GZg1aRwmAXQ==",
 			"dev": true,
 			"license": "MIT",
-			"peer": true,
 			"dependencies": {
 				"@sveltejs/vite-plugin-svelte-inspector": "^4.0.1",
 				"debug": "^4.4.1",
@@ -1520,7 +1518,6 @@
 			"integrity": "sha512-LCCV0HdSZZZb34qifBsyWlUmok6W7ouER+oQIGBScS8EsZsQbrtFTUrDX4hOl+CS6p7cnNC4td+qrSVGSCTUfQ==",
 			"dev": true,
 			"license": "MIT",
-			"peer": true,
 			"dependencies": {
 				"undici-types": "~6.21.0"
 			}
@@ -1530,7 +1527,6 @@
 			"resolved": "https://registry.npmjs.org/acorn/-/acorn-8.15.0.tgz",
 			"integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==",
 			"license": "MIT",
-			"peer": true,
 			"bin": {
 				"acorn": "bin/acorn"
 			},
@@ -1943,7 +1939,6 @@
 			"integrity": "sha512-fmTRWbNMmsmWq6xJV8D19U/gw/bwrHfNXxrIN+HfZgnzqTHp9jOmKMhsTUjXOJnZOdZY9Q28y4yebKzqDKlxlQ==",
 			"dev": true,
 			"license": "ISC",
-			"peer": true,
 			"engines": {
 				"node": ">=12"
 			}
@@ -2651,7 +2646,6 @@
 			"integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
 			"dev": true,
 			"license": "MIT",
-			"peer": true,
 			"engines": {
 				"node": ">=12"
 			},
@@ -2839,7 +2833,6 @@
 			"resolved": "https://registry.npmjs.org/svelte/-/svelte-5.45.3.tgz",
 			"integrity": "sha512-ngKXNhNvwPzF43QqEhDOue7TQTrG09em1sd4HBxVF0Wr2gopAmdEWan+rgbdgK4fhBtSOTJO8bYU4chUG7VXZQ==",
 			"license": "MIT",
-			"peer": true,
 			"dependencies": {
 				"@jridgewell/remapping": "^2.3.4",
 				"@jridgewell/sourcemap-codec": "^1.5.0",
@@ -2984,7 +2977,6 @@
 			"integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==",
 			"dev": true,
 			"license": "Apache-2.0",
-			"peer": true,
 			"bin": {
 				"tsc": "bin/tsc",
 				"tsserver": "bin/tsserver"
@@ -3006,7 +2998,6 @@
 			"integrity": "sha512-+Oxm7q9hDoLMyJOYfUYBuHQo+dkAloi33apOPP56pzj+vsdJDzr+j1NISE5pyaAuKL4A3UD34qd0lx5+kfKp2g==",
 			"dev": true,
 			"license": "MIT",
-			"peer": true,
 			"dependencies": {
 				"esbuild": "^0.25.0",
 				"fdir": "^6.4.4",

dashboard/src/lib/stores/app.svelte.ts

@@ -71,44 +71,46 @@ export interface Instance {
 	};
 }
 
-interface RawNodeProfile {
+// Granular node state types from the new state structure
+interface RawNodeIdentity {
 	modelId?: string;
 	chipId?: string;
 	friendlyName?: string;
-	networkInterfaces?: Array<{
-		name?: string;
-		ipAddress?: string;
-		addresses?: Array<{ address?: string } | string>;
-		ipv4?: string;
-		ipv6?: string;
-		ipAddresses?: string[];
-		ips?: string[];
-	}>;
-	memory?: {
-		ramTotal?: { inBytes: number };
-		ramAvailable?: { inBytes: number };
-		swapTotal?: { inBytes: number };
-		swapAvailable?: { inBytes: number };
-	};
-	system?: {
-		gpuUsage?: number;
-		temp?: number;
-		sysPower?: number;
-	};
 }
 
-interface RawTopologyNode {
-	nodeId: string;
-	nodeProfile?: RawNodeProfile;
+interface RawMemoryUsage {
+	ramTotal?: { inBytes: number };
+	ramAvailable?: { inBytes: number };
+	swapTotal?: { inBytes: number };
+	swapAvailable?: { inBytes: number };
+}
+
+interface RawSystemPerformanceProfile {
+	gpuUsage?: number;
+	temp?: number;
+	sysPower?: number;
+	pcpuUsage?: number;
+	ecpuUsage?: number;
+}
+
+interface RawNetworkInterfaceInfo {
+	name?: string;
+	ipAddress?: string;
+	addresses?: Array<{ address?: string } | string>;
+	ipv4?: string;
+	ipv6?: string;
+	ipAddresses?: string[];
+	ips?: string[];
+}
+
+interface RawNodeNetworkInfo {
+	interfaces?: RawNetworkInterfaceInfo[];
 }
 
-// New connection edge types from Python SocketConnection/RDMAConnection
 interface RawSocketConnection {
 	sinkMultiaddr?: {
 		address?: string;
-		// Multiaddr uses snake_case (no camelCase alias)
 		ip_address?: string;
-		ipAddress?: string; // fallback in case it changes
 		address_type?: string;
 		port?: number;
 	};
@@ -125,14 +127,10 @@ type RawConnectionEdge = RawSocketConnection | RawRDMAConnection;
 type RawConnectionsMap = Record<string, Record<string, RawConnectionEdge[]>>;
 
 interface RawTopology {
-	// nodes can be array of strings (node IDs) or array of objects with nodeId/nodeProfile
-	nodes: (string | RawTopologyNode)[];
-	// New nested mapping format
+	nodes: string[];
 	connections?: RawConnectionsMap;
 }
 
-type RawNodeProfiles = Record<string, RawNodeProfile>;
-
 export interface DownloadProgress {
 	totalBytes: number;
 	downloadedBytes: number;
@@ -187,7 +185,11 @@ interface RawStateResponse {
 	>;
 	runners?: Record<string, unknown>;
 	downloads?: Record<string, unknown[]>;
-	nodeProfiles?: RawNodeProfiles;
+	// New granular node state fields
+	nodeIdentities?: Record<string, RawNodeIdentity>;
+	nodeMemory?: Record<string, RawMemoryUsage>;
+	nodeSystem?: Record<string, RawSystemPerformanceProfile>;
+	nodeNetwork?: Record<string, RawNodeNetworkInfo>;
 }
 
 export interface MessageAttachment {
@@ -222,65 +224,69 @@ export interface Conversation {
 
 const STORAGE_KEY = "exo-conversations";
 
+interface GranularNodeState {
+	nodeIdentities?: Record<string, RawNodeIdentity>;
+	nodeMemory?: Record<string, RawMemoryUsage>;
+	nodeSystem?: Record<string, RawSystemPerformanceProfile>;
+	nodeNetwork?: Record<string, RawNodeNetworkInfo>;
+}
+
+function transformNetworkInterface(iface: RawNetworkInterfaceInfo): {
+	name?: string;
+	addresses: string[];
+} {
+	const addresses: string[] = [];
+	if (iface.ipAddress && typeof iface.ipAddress === "string") {
+		addresses.push(iface.ipAddress);
+	}
+	if (Array.isArray(iface.addresses)) {
+		for (const addr of iface.addresses) {
+			if (typeof addr === "string") addresses.push(addr);
+			else if (addr && typeof addr === "object" && addr.address)
+				addresses.push(addr.address);
+		}
+	}
+	if (Array.isArray(iface.ipAddresses)) {
+		addresses.push(
+			...iface.ipAddresses.filter((a): a is string => typeof a === "string"),
+		);
+	}
+	if (Array.isArray(iface.ips)) {
+		addresses.push(
+			...iface.ips.filter((a): a is string => typeof a === "string"),
+		);
+	}
+	if (iface.ipv4 && typeof iface.ipv4 === "string") addresses.push(iface.ipv4);
+	if (iface.ipv6 && typeof iface.ipv6 === "string") addresses.push(iface.ipv6);
+
+	return {
+		name: iface.name,
+		addresses: Array.from(new Set(addresses)),
+	};
+}
+
 function transformTopology(
 	raw: RawTopology,
-	profiles?: RawNodeProfiles,
+	granularState: GranularNodeState,
 ): TopologyData {
 	const nodes: Record<string, NodeInfo> = {};
 	const edges: TopologyEdge[] = [];
 
-	// Handle nodes - can be array of strings (node IDs) or array of objects with nodeId/nodeProfile
-	for (const node of raw.nodes || []) {
-		// Determine the node ID - could be a string or an object with nodeId property
-		const nodeId = typeof node === "string" ? node : node.nodeId;
+	for (const nodeId of raw.nodes || []) {
 		if (!nodeId) continue;
 
-		// Get the profile - from the separate profiles map or from the node object itself
-		const profileFromMap = profiles?.[nodeId];
-		const profileFromNode =
-			typeof node === "object" ? node.nodeProfile : undefined;
-		const profile = { ...(profileFromNode ?? {}), ...(profileFromMap ?? {}) };
+		// Get data from granular state mappings
+		const identity = granularState.nodeIdentities?.[nodeId];
+		const memory = granularState.nodeMemory?.[nodeId];
+		const system = granularState.nodeSystem?.[nodeId];
+		const network = granularState.nodeNetwork?.[nodeId];
 
-		const ramTotal = profile?.memory?.ramTotal?.inBytes ?? 0;
-		const ramAvailable = profile?.memory?.ramAvailable?.inBytes ?? 0;
+		const ramTotal = memory?.ramTotal?.inBytes ?? 0;
+		const ramAvailable = memory?.ramAvailable?.inBytes ?? 0;
 		const ramUsage = Math.max(ramTotal - ramAvailable, 0);
 
-		const networkInterfaces = (profile?.networkInterfaces || []).map(
-			(iface) => {
-				const addresses: string[] = [];
-				if (iface.ipAddress && typeof iface.ipAddress === "string") {
-					addresses.push(iface.ipAddress);
-				}
-				if (Array.isArray(iface.addresses)) {
-					for (const addr of iface.addresses) {
-						if (typeof addr === "string") addresses.push(addr);
-						else if (addr && typeof addr === "object" && addr.address)
-							addresses.push(addr.address);
-					}
-				}
-				if (Array.isArray(iface.ipAddresses)) {
-					addresses.push(
-						...iface.ipAddresses.filter(
-							(a): a is string => typeof a === "string",
-						),
-					);
-				}
-				if (Array.isArray(iface.ips)) {
-					addresses.push(
-						...iface.ips.filter((a): a is string => typeof a === "string"),
-					);
-				}
-				if (iface.ipv4 && typeof iface.ipv4 === "string")
-					addresses.push(iface.ipv4);
-				if (iface.ipv6 && typeof iface.ipv6 === "string")
-					addresses.push(iface.ipv6);
-
-				return {
-					name: iface.name,
-					addresses: Array.from(new Set(addresses)),
-				};
-			},
-		);
+		const rawInterfaces = network?.interfaces || [];
+		const networkInterfaces = rawInterfaces.map(transformNetworkInterface);
 
 		const ipToInterface: Record<string, string> = {};
 		for (const iface of networkInterfaces) {
@@ -291,8 +297,8 @@ function transformTopology(
 
 		nodes[nodeId] = {
 			system_info: {
-				model_id: profile?.modelId ?? "Unknown",
-				chip: profile?.chipId,
+				model_id: identity?.modelId ?? "Unknown",
+				chip: identity?.chipId,
 				memory: ramTotal,
 			},
 			network_interfaces: networkInterfaces,
@@ -303,17 +309,15 @@ function transformTopology(
 					ram_total: ramTotal,
 				},
 				temp:
-					profile?.system?.temp !== undefined
-						? { gpu_temp_avg: profile.system.temp }
+					system?.temp !== undefined
+						? { gpu_temp_avg: system.temp }
 						: undefined,
 				gpu_usage:
-					profile?.system?.gpuUsage !== undefined
-						? [0, profile.system.gpuUsage]
-						: undefined,
-				sys_power: profile?.system?.sysPower,
+					system?.gpuUsage !== undefined ? [0, system.gpuUsage] : undefined,
+				sys_power: system?.sysPower,
 			},
 			last_macmon_update: Date.now() / 1000,
-			friendly_name: profile?.friendlyName,
+			friendly_name: identity?.friendlyName,
 		};
 	}
 
@@ -325,19 +329,15 @@ function transformTopology(
 			for (const [sink, edgeList] of Object.entries(sinks)) {
 				if (!Array.isArray(edgeList)) continue;
 				for (const edge of edgeList) {
-					// Extract IP from SocketConnection (uses snake_case: ip_address)
 					let sendBackIp: string | undefined;
 					if (edge && typeof edge === "object" && "sinkMultiaddr" in edge) {
 						const multiaddr = edge.sinkMultiaddr;
 						if (multiaddr) {
-							// Try both snake_case (actual) and camelCase (in case it changes)
 							sendBackIp =
 								multiaddr.ip_address ||
-								multiaddr.ipAddress ||
 								extractIpFromMultiaddr(multiaddr.address);
 						}
 					}
-					// RDMAConnection (sourceRdmaIface/sinkRdmaIface) has no IP - edge just shows connection exists
 
 					if (nodes[source] && nodes[sink] && source !== sink) {
 						edges.push({ source, target: sink, sendBackIp });
@@ -898,7 +898,12 @@ class AppStore {
 			const data: RawStateResponse = await response.json();
 
 			if (data.topology) {
-				this.topologyData = transformTopology(data.topology, data.nodeProfiles);
+				this.topologyData = transformTopology(data.topology, {
+					nodeIdentities: data.nodeIdentities,
+					nodeMemory: data.nodeMemory,
+					nodeSystem: data.nodeSystem,
+					nodeNetwork: data.nodeNetwork,
+				});
 			}
 			if (data.instances) {
 				this.instances = data.instances;

dashboard/src/routes/+page.svelte

@@ -434,8 +434,8 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 		const shardData = shardObj[shardKeys[0]] as Record<string, unknown>;
 		if (!shardData) return null;
 		
-		// Model meta is nested: shard.model_meta.model_id
-		const modelMeta = shardData.model_meta ?? shardData.modelMeta;
+		// Model meta is nested: shard.model_card.model_id
+		const modelMeta = shardData.model_card ?? shardData.modelCard;
 		if (!modelMeta || typeof modelMeta !== 'object') return null;
 		
 		const meta = modelMeta as Record<string, unknown>;

dashboard/src/routes/downloads/+page.svelte

@@ -98,7 +98,7 @@
 		const shardData = shardObj[shardKeys[0]] as Record<string, unknown>;
 		if (!shardData) return null;
 
-		const modelMeta = shardData.model_meta ?? shardData.modelMeta;
+		const modelMeta = shardData.model_card ?? shardData.modelCard;
 		if (!modelMeta || typeof modelMeta !== 'object') return null;
 
 		const meta = modelMeta as Record<string, unknown>;
@@ -190,7 +190,7 @@
 						const shardKeys = Object.keys(shardObj);
 						if (shardKeys.length !== 1) return null;
 						const shardData = shardObj[shardKeys[0]] as Record<string, unknown>;
-						const modelMeta = shardData?.model_meta ?? shardData?.modelMeta;
+						const modelMeta = shardData?.model_card ?? shardData?.modelCard;
 						if (!modelMeta || typeof modelMeta !== 'object') return null;
 						const meta = modelMeta as Record<string, unknown>;
 						return (meta.prettyName as string) ?? null;

packaging/pyinstaller/exo.spec

@@ -10,6 +10,7 @@ PROJECT_ROOT = Path.cwd()
 SOURCE_ROOT = PROJECT_ROOT / "src"
 ENTRYPOINT = SOURCE_ROOT / "exo" / "__main__.py"
 DASHBOARD_DIR = PROJECT_ROOT / "dashboard" / "build"
+RESOURCES_DIR = PROJECT_ROOT / "resources"
 EXO_SHARED_MODELS_DIR = SOURCE_ROOT / "exo" / "shared" / "models"
 
 if not ENTRYPOINT.is_file():
@@ -18,6 +19,9 @@ if not ENTRYPOINT.is_file():
 if not DASHBOARD_DIR.is_dir():
     raise SystemExit(f"Dashboard assets are missing: {DASHBOARD_DIR}")
 
+if not RESOURCES_DIR.is_dir():
+    raise SystemExit(f"Resources are missing: {RESOURCES_DIR}")
+
 if not EXO_SHARED_MODELS_DIR.is_dir():
     raise SystemExit(f"Shared model assets are missing: {EXO_SHARED_MODELS_DIR}")
 
@@ -58,6 +62,7 @@ HIDDEN_IMPORTS = sorted(
 
 DATAS: list[tuple[str, str]] = [
     (str(DASHBOARD_DIR), "dashboard"),
+    (str(RESOURCES_DIR), "resources"),
     (str(MLX_LIB_DIR), "mlx/lib"),
     (str(EXO_SHARED_MODELS_DIR), "exo/shared/models"),
 ]

pyproject.toml

@@ -17,8 +17,8 @@ dependencies = [
     "loguru>=0.7.3",
     "exo_pyo3_bindings", # rust bindings
     "anyio==4.11.0",
-    "mlx==0.30.1; sys_platform == 'darwin'",
-    "mlx[cpu]==0.30.1; sys_platform == 'linux'",
+    "mlx==0.30.3; sys_platform == 'darwin'",
+    "mlx[cpu]==0.30.3; sys_platform == 'linux'",
     "mlx-lm @ git+https://github.com/AlexCheema/mlx-lm.git@fix-transformers-5.0.0rc2",
     "tiktoken>=0.12.0", # required for kimi k2 tokenizer
     "hypercorn>=0.18.0",

resources/mlx-community--DeepSeek-V3.1-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/DeepSeek-V3.1-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[storage_size]
+in_bytes = 405874409472

resources/mlx-community--DeepSeek-V3.1-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/DeepSeek-V3.1-8bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[storage_size]
+in_bytes = 765577920512

resources/mlx-community--GLM-4.5-Air-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.5-Air-8bit"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = false
+
+[storage_size]
+in_bytes = 122406567936

resources/mlx-community--GLM-4.5-Air-bf16.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.5-Air-bf16"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 229780750336

resources/mlx-community--GLM-4.7-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.7-4bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[storage_size]
+in_bytes = 198556925568

resources/mlx-community--GLM-4.7-6bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.7-6bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[storage_size]
+in_bytes = 286737579648

resources/mlx-community--GLM-4.7-8bit-gs32.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/GLM-4.7-8bit-gs32"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+
+[storage_size]
+in_bytes = 396963397248

resources/mlx-community--Kimi-K2-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Kimi-K2-Instruct-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[storage_size]
+in_bytes = 620622774272

resources/mlx-community--Kimi-K2-Thinking.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Kimi-K2-Thinking"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+
+[storage_size]
+in_bytes = 706522120192

resources/mlx-community--Llama-3.2-1B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+n_layers = 16
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 729808896

resources/mlx-community--Llama-3.2-3B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+
+[storage_size]
+in_bytes = 1863319552

resources/mlx-community--Llama-3.2-3B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-8bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+
+[storage_size]
+in_bytes = 3501195264

resources/mlx-community--Llama-3.3-70B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[storage_size]
+in_bytes = 40652242944

resources/mlx-community--Llama-3.3-70B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-8bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[storage_size]
+in_bytes = 76799803392

resources/mlx-community--Meta-Llama-3.1-70B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[storage_size]
+in_bytes = 40652242944

resources/mlx-community--Meta-Llama-3.1-8B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 4637851648

resources/mlx-community--Meta-Llama-3.1-8B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 8954839040

resources/mlx-community--Meta-Llama-3.1-8B-Instruct-bf16.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 16882073600

resources/mlx-community--MiniMax-M2.1-3bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/MiniMax-M2.1-3bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+
+[storage_size]
+in_bytes = 100086644736

resources/mlx-community--MiniMax-M2.1-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/MiniMax-M2.1-8bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+
+[storage_size]
+in_bytes = 242986745856

resources/mlx-community--Qwen3-0.6B-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-0.6B-4bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+
+[storage_size]
+in_bytes = 342884352

resources/mlx-community--Qwen3-0.6B-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-0.6B-8bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+
+[storage_size]
+in_bytes = 698351616

resources/mlx-community--Qwen3-235B-A22B-Instruct-2507-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 141733920768

resources/mlx-community--Qwen3-235B-A22B-Instruct-2507-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+
+[storage_size]
+in_bytes = 268435456000

resources/mlx-community--Qwen3-30B-A3B-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-30B-A3B-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 17612931072

resources/mlx-community--Qwen3-30B-A3B-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-30B-A3B-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 33279705088

resources/mlx-community--Qwen3-Coder-480B-A35B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+
+[storage_size]
+in_bytes = 289910292480

resources/mlx-community--Qwen3-Coder-480B-A35B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+
+[storage_size]
+in_bytes = 579820584960

resources/mlx-community--Qwen3-Next-80B-A3B-Instruct-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 46976204800

resources/mlx-community--Qwen3-Next-80B-A3B-Instruct-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 88814387200

resources/mlx-community--Qwen3-Next-80B-A3B-Thinking-4bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 88814387200

resources/mlx-community--Qwen3-Next-80B-A3B-Thinking-8bit.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+
+[storage_size]
+in_bytes = 88814387200

resources/mlx-community--gpt-oss-120b-MXFP4-Q8.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/gpt-oss-120b-MXFP4-Q8"
+n_layers = 36
+hidden_size = 2880
+supports_tensor = true
+
+[storage_size]
+in_bytes = 70652212224

resources/mlx-community--gpt-oss-20b-MXFP4-Q8.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/gpt-oss-20b-MXFP4-Q8"
+n_layers = 24
+hidden_size = 2880
+supports_tensor = true
+
+[storage_size]
+in_bytes = 12025908224

resources/mlx-community--llama-3.3-70b-instruct-fp16.toml

@@ -0,0 +1,7 @@
+model_id = "mlx-community/llama-3.3-70b-instruct-fp16"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+
+[storage_size]
+in_bytes = 144383672320

src/exo/master/api.py

@@ -1,5 +1,6 @@
 import time
 from collections.abc import AsyncGenerator
+from dataclasses import dataclass, field
 from http import HTTPStatus
 from typing import cast
 
@@ -19,8 +20,7 @@ from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
 from exo.shared.election import ElectionMessage
 from exo.shared.logging import InterceptLogger
-from exo.shared.models.model_cards import MODEL_CARDS
-from exo.shared.models.model_meta import get_model_meta
+from exo.shared.models.model_cards import ModelCard, ModelId, get_model_cards
 from exo.shared.types.api import (
     BenchChatCompletionResponse,
     BenchChatCompletionTaskParams,
@@ -59,14 +59,13 @@ from exo.shared.types.events import (
     IndexedEvent,
 )
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.state import State
 from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.banner import print_startup_banner
 from exo.utils.channels import Receiver, Sender, channel
-from exo.utils.dashboard_path import find_dashboard
+from exo.utils.dashboard_path import RuntimeResources, find_directory
 from exo.utils.event_buffer import OrderedBuffer
 
 
@@ -87,57 +86,52 @@ def chunk_to_response(
     )
 
 
-async def resolve_model_meta(model_id: str) -> ModelMetadata:
-    if model_id in MODEL_CARDS:
-        model_card = MODEL_CARDS[model_id]
-        return model_card.metadata
-    else:
-        return await get_model_meta(model_id)
-
-
+@dataclass(eq=False)
 class API:
-    def __init__(
-        self,
+    node_id: NodeId
+    session_id: SessionId
+    port: int
+    app: FastAPI
+    global_event_receiver: Receiver[ForwarderEvent]
+    command_sender: Sender[ForwarderCommand]
+    election_receiver: Receiver[ElectionMessage]
+    state = field(init=False, default_factory=State)
+    _event_log: list[Event] = field(init=False, default_factory=list)
+    event_buffer: OrderedBuffer[Event] = field(init=False, default_factory=OrderedBuffer)
+    _chat_completion_queues: dict[CommandId, Sender[TokenChunk]] = field(init=False, default_factory=dict)
+    _tg: TaskGroup = field(init=False, default_factory=create_task_group)
+    last_completed_election: int = field(init=False, default=0)
+    paused: bool = field(init=False, default = False)
+    paused_ev: anyio.Event = field(init=False, default_factory=anyio.Event)
+
+    @classmethod
+    async def create(
+        cls,
         node_id: NodeId,
         session_id: SessionId,
         *,
         port: int,
-        # Ideally this would be a MasterForwarderEvent but type system says no :(
         global_event_receiver: Receiver[ForwarderEvent],
         command_sender: Sender[ForwarderCommand],
-        # This lets us pause the API if an election is running
         election_receiver: Receiver[ElectionMessage],
     ) -> None:
-        self.state = State()
-        self._event_log: list[Event] = []
-        self.command_sender = command_sender
-        self.global_event_receiver = global_event_receiver
-        self.election_receiver = election_receiver
-        self.event_buffer: OrderedBuffer[Event] = OrderedBuffer[Event]()
-        self.node_id: NodeId = node_id
-        self.session_id: SessionId = session_id
-        self.last_completed_election: int = 0
-        self.port = port
-
-        self.paused: bool = False
-        self.paused_ev: anyio.Event = anyio.Event()
-
-        self.app = FastAPI()
-        self._setup_exception_handlers()
-        self._setup_cors()
-        self._setup_routes()
-
-        self.app.mount(
+        app = FastAPI()
+        app.mount(
             "/",
             StaticFiles(
-                directory=find_dashboard(),
+                directory=await find_directory(RuntimeResources.Dashboard),
                 html=True,
             ),
             name="dashboard",
         )
 
-        self._chat_completion_queues: dict[CommandId, Sender[TokenChunk]] = {}
-        self._tg: TaskGroup | None = None
+        cls(node_id, session_id, port, app, global_event_receiver, command_sender, election_receiver)
+
+    def __post_init__(self) -> None:
+        self._setup_exception_handlers()
+        self._setup_cors()
+        self._setup_routes()
+
 
     def reset(self, new_session_id: SessionId, result_clock: int):
         logger.info("Resetting API State")
@@ -197,7 +191,7 @@ class API:
 
     async def place_instance(self, payload: PlaceInstanceParams):
         command = PlaceInstance(
-            model_meta=await resolve_model_meta(payload.model_id),
+            model_card=await resolve_model_card(payload.model_id),
             sharding=payload.sharding,
             instance_meta=payload.instance_meta,
             min_nodes=payload.min_nodes,
@@ -207,15 +201,15 @@ class API:
         return CreateInstanceResponse(
             message="Command received.",
             command_id=command.command_id,
-            model_meta=command.model_meta,
+            model_card=command.model_card,
         )
 
     async def create_instance(
         self, payload: CreateInstanceParams
     ) -> CreateInstanceResponse:
         instance = payload.instance
-        model_meta = await resolve_model_meta(instance.shard_assignments.model_id)
-        required_memory = model_meta.storage_size
+        model_card = await ModelCard.from_hf(instance.shard_assignments.model_id)
+        required_memory = model_card.storage_size
         available_memory = self._calculate_total_available_memory()
 
         if required_memory > available_memory:
@@ -232,7 +226,7 @@ class API:
         return CreateInstanceResponse(
             message="Command received.",
             command_id=command.command_id,
-            model_meta=model_meta,
+            model_card=model_card,
         )
 
     async def get_placement(
@@ -242,17 +236,18 @@ class API:
         instance_meta: InstanceMeta = InstanceMeta.MlxRing,
         min_nodes: int = 1,
     ) -> Instance:
-        model_meta = await resolve_model_meta(model_id)
+        model_card = await resolve_model_card(model_id)
 
         try:
             placements = get_instance_placements(
                 PlaceInstance(
-                    model_meta=model_meta,
+                    model_card=model_card,
                     sharding=sharding,
                     instance_meta=instance_meta,
                     min_nodes=min_nodes,
                 ),
-                node_profiles=self.state.node_profiles,
+                node_memory=self.state.node_memory,
+                node_network=self.state.node_network,
                 topology=self.state.topology,
                 current_instances=self.state.instances,
             )
@@ -279,7 +274,7 @@ class API:
         if len(list(self.state.topology.list_nodes())) == 0:
             return PlacementPreviewResponse(previews=[])
 
-        cards = [card for card in MODEL_CARDS.values() if card.short_id == model_id]
+        cards = [card for card in await get_model_cards() if card.short_id == model_id]
         if not cards:
             raise HTTPException(status_code=404, detail=f"Model {model_id} not found")
 
@@ -297,33 +292,33 @@ class API:
         # TODO: PDD
         # instance_combinations.append((Sharding.PrefillDecodeDisaggregation, InstanceMeta.MlxRing, 1))
 
-        for card in cards:
-            model_meta = card.metadata
+        for model_card in cards:
             for sharding, instance_meta, min_nodes in instance_combinations:
                 try:
                     placements = get_instance_placements(
                         PlaceInstance(
-                            model_meta=model_meta,
+                            model_card=model_card,
                             sharding=sharding,
                             instance_meta=instance_meta,
                             min_nodes=min_nodes,
                         ),
-                        node_profiles=self.state.node_profiles,
+                        node_memory=self.state.node_memory,
+                        node_network=self.state.node_network,
                         topology=self.state.topology,
                         current_instances=self.state.instances,
                     )
                 except ValueError as exc:
-                    if (card.model_id, sharding, instance_meta, 0) not in seen:
+                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
                         previews.append(
                             PlacementPreview(
-                                model_id=card.model_id,
+                                model_id=model_card.model_id,
                                 sharding=sharding,
                                 instance_meta=instance_meta,
                                 instance=None,
                                 error=str(exc),
                             )
                         )
-                    seen.add((card.model_id, sharding, instance_meta, 0))
+                    seen.add((model_card.model_id, sharding, instance_meta, 0))
                     continue
 
                 current_ids = set(self.state.instances.keys())
@@ -334,17 +329,17 @@ class API:
                 ]
 
                 if len(new_instances) != 1:
-                    if (card.model_id, sharding, instance_meta, 0) not in seen:
+                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
                         previews.append(
                             PlacementPreview(
-                                model_id=card.model_id,
+                                model_id=model_card.model_id,
                                 sharding=sharding,
                                 instance_meta=instance_meta,
                                 instance=None,
                                 error="Expected exactly one new instance from placement",
                             )
                         )
-                    seen.add((card.model_id, sharding, instance_meta, 0))
+                    seen.add((model_card.model_id, sharding, instance_meta, 0))
                     continue
 
                 instance = new_instances[0]
@@ -353,7 +348,7 @@ class API:
 
                 memory_delta_by_node: dict[str, int] = {}
                 if node_ids:
-                    total_bytes = model_meta.storage_size.in_bytes
+                    total_bytes = model_card.storage_size.in_bytes
                     per_node = total_bytes // len(node_ids)
                     remainder = total_bytes % len(node_ids)
                     for index, node_id in enumerate(sorted(node_ids, key=str)):
@@ -361,14 +356,14 @@ class API:
                         memory_delta_by_node[str(node_id)] = per_node + extra
 
                 if (
-                    card.model_id,
+                    model_card.model_id,
                     sharding,
                     instance_meta,
                     len(node_ids),
                 ) not in seen:
                     previews.append(
                         PlacementPreview(
-                            model_id=card.model_id,
+                            model_id=model_card.model_id,
                             sharding=sharding,
                             instance_meta=instance_meta,
                             instance=instance,
@@ -376,7 +371,7 @@ class API:
                             error=None,
                         )
                     )
-                seen.add((card.model_id, sharding, instance_meta, len(node_ids)))
+                seen.add((model_card.model_id, sharding, instance_meta, len(node_ids)))
 
         return PlacementPreviewResponse(previews=previews)
 
@@ -551,8 +546,8 @@ class API:
         self, payload: ChatCompletionTaskParams
     ) -> ChatCompletionResponse | StreamingResponse:
         """Handle chat completions, supporting both streaming and non-streaming responses."""
-        model_meta = await resolve_model_meta(payload.model)
-        payload.model = model_meta.model_id
+        model_card = await resolve_model_card(payload.model)
+        payload.model = model_card.model_id
 
         if not any(
             instance.shard_assignments.model_id == payload.model
@@ -578,8 +573,8 @@ class API:
     async def bench_chat_completions(
         self, payload: BenchChatCompletionTaskParams
     ) -> BenchChatCompletionResponse:
-        model_meta = await resolve_model_meta(payload.model)
-        payload.model = model_meta.model_id
+        model_card = await resolve_model_card(payload.model)
+        payload.model = model_card.model_id
 
         if not any(
             instance.shard_assignments.model_id == payload.model
@@ -602,8 +597,8 @@ class API:
         """Calculate total available memory across all nodes in bytes."""
         total_available = Memory()
 
-        for profile in self.state.node_profiles.values():
-            total_available += profile.memory.ram_available
+        for memory in self.state.node_memory.values():
+            total_available += memory.ram_available
 
         return total_available
 
@@ -612,15 +607,15 @@ class API:
         return ModelList(
             data=[
                 ModelListModel(
-                    id=card.short_id,
+                    id=card.model_id,
                     hugging_face_id=card.model_id,
-                    name=card.name,
-                    description=card.description,
-                    tags=card.tags,
-                    storage_size_megabytes=int(card.metadata.storage_size.in_mb),
-                    supports_tensor=card.metadata.supports_tensor,
+                    name=card.model_id.short(),
+                    description="",
+                    tags=[],
+                    storage_size_megabytes=int(card.storage_size.in_mb),
+                    supports_tensor=card.supports_tensor,
                 )
-                for card in MODEL_CARDS.values()
+                for card in model_cards()
             ]
         )
 

src/exo/master/main.py

@@ -159,7 +159,8 @@ class Master:
                                 command,
                                 self.state.topology,
                                 self.state.instances,
-                                self.state.node_profiles,
+                                self.state.node_memory,
+                                self.state.node_network,
                             )
                             transition_events = get_transition_events(
                                 self.state.instances, placement

src/exo/master/placement.py

@@ -14,6 +14,7 @@ from exo.master.placement_utils import (
     get_shard_assignments,
     get_smallest_cycles,
 )
+from exo.shared.models.model_cards import ModelId
 from exo.shared.topology import Topology
 from exo.shared.types.commands import (
     CreateInstance,
@@ -23,8 +24,7 @@ from exo.shared.types.commands import (
 from exo.shared.types.common import NodeId
 from exo.shared.types.events import Event, InstanceCreated, InstanceDeleted
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId
-from exo.shared.types.profiling import NodePerformanceProfile
+from exo.shared.types.profiling import MemoryUsage, NodeNetworkInfo
 from exo.shared.types.worker.instances import (
     Instance,
     InstanceId,
@@ -54,32 +54,33 @@ def place_instance(
     command: PlaceInstance,
     topology: Topology,
     current_instances: Mapping[InstanceId, Instance],
-    node_profiles: Mapping[NodeId, NodePerformanceProfile],
+    node_memory: Mapping[NodeId, MemoryUsage],
+    node_network: Mapping[NodeId, NodeNetworkInfo],
 ) -> dict[InstanceId, Instance]:
     cycles = topology.get_cycles()
     candidate_cycles = list(filter(lambda it: len(it) >= command.min_nodes, cycles))
     cycles_with_sufficient_memory = filter_cycles_by_memory(
-        candidate_cycles, node_profiles, command.model_meta.storage_size
+        candidate_cycles, node_memory, command.model_card.storage_size
     )
     if len(cycles_with_sufficient_memory) == 0:
         raise ValueError("No cycles found with sufficient memory")
 
     if command.sharding == Sharding.Tensor:
-        if not command.model_meta.supports_tensor:
+        if not command.model_card.supports_tensor:
             raise ValueError(
-                f"Requested Tensor sharding but this model does not support tensor parallelism: {command.model_meta.model_id}"
+                f"Requested Tensor sharding but this model does not support tensor parallelism: {command.model_card.model_id}"
             )
         # TODO: the condition here for tensor parallel is not correct, but it works good enough for now.
         cycles_with_sufficient_memory = [
             cycle
             for cycle in cycles_with_sufficient_memory
-            if command.model_meta.hidden_size % len(cycle) == 0
+            if command.model_card.hidden_size % len(cycle) == 0
         ]
         if not cycles_with_sufficient_memory:
             raise ValueError(
-                f"No tensor sharding found for model with hidden_size {command.model_meta.hidden_size} candidate cycles"
+                f"No tensor sharding found for model with hidden_size {command.model_card.hidden_size} candidate cycles"
             )
-    if command.sharding == Sharding.Pipeline and command.model_meta.model_id == ModelId(
+    if command.sharding == Sharding.Pipeline and command.model_card.model_id == ModelId(
         "mlx-community/DeepSeek-V3.1-8bit"
     ):
         raise ValueError(
@@ -104,13 +105,13 @@ def place_instance(
     selected_cycle = max(
         cycles_with_leaf_nodes if cycles_with_leaf_nodes != [] else smallest_cycles,
         key=lambda cycle: sum(
-            (node_profiles[node_id].memory.ram_available for node_id in cycle),
+            (node_memory[node_id].ram_available for node_id in cycle),
             start=Memory(),
         ),
     )
 
     shard_assignments = get_shard_assignments(
-        command.model_meta, selected_cycle, command.sharding, node_profiles
+        command.model_card, selected_cycle, command.sharding, node_memory
     )
 
     cycle_digraph: Topology = topology.get_subgraph_from_nodes(selected_cycle.node_ids)
@@ -136,7 +137,7 @@ def place_instance(
                 coordinator=selected_cycle.node_ids[0],
                 coordinator_port=random_ephemeral_port(),
                 cycle_digraph=cycle_digraph,
-                node_profiles=node_profiles,
+                node_network=node_network,
             )
             target_instances[instance_id] = MlxJacclInstance(
                 instance_id=instance_id,
@@ -150,7 +151,7 @@ def place_instance(
                 selected_cycle=selected_cycle,
                 cycle_digraph=cycle_digraph,
                 ephemeral_port=ephemeral_port,
-                node_profiles=node_profiles,
+                node_network=node_network,
             )
             target_instances[instance_id] = MlxRingInstance(
                 instance_id=instance_id,

src/exo/master/placement_utils.py

@@ -2,11 +2,11 @@ from collections.abc import Generator, Mapping
 
 from loguru import logger
 
+from exo.shared.models.model_cards import ModelCard
 from exo.shared.topology import Topology
 from exo.shared.types.common import Host, NodeId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelMetadata
-from exo.shared.types.profiling import NodePerformanceProfile
+from exo.shared.types.profiling import MemoryUsage, NodeNetworkInfo
 from exo.shared.types.topology import Cycle, RDMAConnection, SocketConnection
 from exo.shared.types.worker.runners import RunnerId, ShardAssignments
 from exo.shared.types.worker.shards import (
@@ -19,16 +19,16 @@ from exo.shared.types.worker.shards import (
 
 def filter_cycles_by_memory(
     cycles: list[Cycle],
-    node_profiles: Mapping[NodeId, NodePerformanceProfile],
+    node_memory: Mapping[NodeId, MemoryUsage],
     required_memory: Memory,
 ) -> list[Cycle]:
     filtered_cycles: list[Cycle] = []
     for cycle in cycles:
-        if not all(node in node_profiles for node in cycle):
+        if not all(node in node_memory for node in cycle):
             continue
 
         total_mem = sum(
-            (node_profiles[node_id].memory.ram_available for node_id in cycle.node_ids),
+            (node_memory[node_id].ram_available for node_id in cycle.node_ids),
             start=Memory(),
         )
         if total_mem >= required_memory:
@@ -75,22 +75,21 @@ def allocate_layers_proportionally(
 
 
 def get_shard_assignments_for_pipeline_parallel(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     cycle: Cycle,
-    node_profiles: Mapping[NodeId, NodePerformanceProfile],
+    node_memory: Mapping[NodeId, MemoryUsage],
 ):
     if not cycle.node_ids:
         raise ValueError("Cannot create shard assignments for empty node cycle")
 
     cycle_memory = sum(
-        (node_profiles[node_id].memory.ram_available for node_id in cycle.node_ids),
+        (node_memory[node_id].ram_available for node_id in cycle.node_ids),
         start=Memory(),
     )
-
     if cycle_memory.in_bytes == 0:
         raise ValueError("Cannot create shard assignments: total available memory is 0")
 
-    total_layers = model_meta.n_layers
+    total_layers = model_card.n_layers
     world_size = len(cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
@@ -98,18 +97,18 @@ def get_shard_assignments_for_pipeline_parallel(
     layer_allocations = allocate_layers_proportionally(
         total_layers=total_layers,
         memory_fractions=[
-            node_profiles[node_id].memory.ram_available.in_bytes / cycle_memory.in_bytes
+            node_memory[node_id].ram_available.in_bytes / cycle_memory.in_bytes
             for node_id in cycle.node_ids
         ],
     )
 
     # Validate each node has sufficient memory for its assigned layers
-    memory_per_layer = model_meta.storage_size.in_bytes / total_layers
+    memory_per_layer = model_card.storage_size.in_bytes / total_layers
     for i, (node_id, node_layers) in enumerate(
         zip(cycle.node_ids, layer_allocations, strict=True)
     ):
         required_memory = node_layers * memory_per_layer
-        available_memory = node_profiles[node_id].memory.ram_available.in_bytes
+        available_memory = node_memory[node_id].ram_available.in_bytes
         if required_memory > available_memory:
             raise ValueError(
                 f"Node {i} ({node_id}) has insufficient memory: "
@@ -124,7 +123,7 @@ def get_shard_assignments_for_pipeline_parallel(
         runner_id = RunnerId()
 
         shard = PipelineShardMetadata(
-            model_meta=model_meta,
+            model_card=model_card,
             device_rank=i,
             world_size=world_size,
             start_layer=layers_assigned,
@@ -137,7 +136,7 @@ def get_shard_assignments_for_pipeline_parallel(
         layers_assigned += node_layers
 
     shard_assignments = ShardAssignments(
-        model_id=model_meta.model_id,
+        model_id=model_card.model_id,
         runner_to_shard=runner_to_shard,
         node_to_runner=node_to_runner,
     )
@@ -146,17 +145,17 @@ def get_shard_assignments_for_pipeline_parallel(
 
 
 def get_shard_assignments_for_tensor_parallel(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     cycle: Cycle,
 ):
-    total_layers = model_meta.n_layers
+    total_layers = model_card.n_layers
     world_size = len(cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
 
     for i, node_id in enumerate(cycle):
         shard = TensorShardMetadata(
-            model_meta=model_meta,
+            model_card=model_card,
             device_rank=i,
             world_size=world_size,
             start_layer=0,
@@ -170,7 +169,7 @@ def get_shard_assignments_for_tensor_parallel(
         node_to_runner[node_id] = runner_id
 
     shard_assignments = ShardAssignments(
-        model_id=model_meta.model_id,
+        model_id=model_card.model_id,
         runner_to_shard=runner_to_shard,
         node_to_runner=node_to_runner,
     )
@@ -179,21 +178,21 @@ def get_shard_assignments_for_tensor_parallel(
 
 
 def get_shard_assignments(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     cycle: Cycle,
     sharding: Sharding,
-    node_profiles: Mapping[NodeId, NodePerformanceProfile],
+    node_memory: Mapping[NodeId, MemoryUsage],
 ) -> ShardAssignments:
     match sharding:
         case Sharding.Pipeline:
             return get_shard_assignments_for_pipeline_parallel(
-                model_meta=model_meta,
+                model_card=model_card,
                 cycle=cycle,
-                node_profiles=node_profiles,
+                node_memory=node_memory,
             )
         case Sharding.Tensor:
             return get_shard_assignments_for_tensor_parallel(
-                model_meta=model_meta,
+                model_card=model_card,
                 cycle=cycle,
             )
 
@@ -288,10 +287,10 @@ def _find_connection_ip(
 
 
 def _find_interface_name_for_ip(
-    ip_address: str, node_profile: NodePerformanceProfile
+    ip_address: str, node_network: NodeNetworkInfo
 ) -> str | None:
     """Find the interface name for an IP address on a node (any interface)."""
-    for interface in node_profile.network_interfaces:
+    for interface in node_network.interfaces:
         if interface.ip_address == ip_address:
             return interface.name
 
@@ -302,7 +301,7 @@ def _find_ip_prioritised(
     node_id: NodeId,
     other_node_id: NodeId,
     cycle_digraph: Topology,
-    node_profiles: Mapping[NodeId, NodePerformanceProfile],
+    node_network: Mapping[NodeId, NodeNetworkInfo],
 ) -> str | None:
     # TODO: Actually prioritize in the correct Ethernet > Wifi > Non-TB > TB order.
     """Find an IP address between nodes with prioritization.
@@ -316,7 +315,9 @@ def _find_ip_prioritised(
     ips = list(_find_connection_ip(node_id, other_node_id, cycle_digraph))
     # We expect a unique iface -> ip mapping
     iface_map = {
-        _find_interface_name_for_ip(ip, node_profiles[other_node_id]): ip
+        _find_interface_name_for_ip(
+            ip, node_network.get(other_node_id, NodeNetworkInfo())
+        ): ip
         for ip, _ in ips
     }
 
@@ -345,7 +346,7 @@ def get_mlx_ring_hosts_by_node(
     selected_cycle: Cycle,
     cycle_digraph: Topology,
     ephemeral_port: int,
-    node_profiles: Mapping[NodeId, NodePerformanceProfile],
+    node_network: Mapping[NodeId, NodeNetworkInfo],
 ) -> dict[NodeId, list[Host]]:
     """Generate per-node host lists for MLX ring backend.
 
@@ -377,7 +378,7 @@ def get_mlx_ring_hosts_by_node(
                 continue
 
             connection_ip = _find_ip_prioritised(
-                node_id, other_node_id, cycle_digraph, node_profiles
+                node_id, other_node_id, cycle_digraph, node_network
             )
             if connection_ip is None:
                 logger.warning(
@@ -398,7 +399,7 @@ def get_mlx_jaccl_coordinators(
     coordinator: NodeId,
     coordinator_port: int,
     cycle_digraph: Topology,
-    node_profiles: Mapping[NodeId, NodePerformanceProfile],
+    node_network: Mapping[NodeId, NodeNetworkInfo],
 ) -> dict[NodeId, str]:
     """Get the coordinator addresses for MLX JACCL (rank 0 device).
 
@@ -411,7 +412,7 @@ def get_mlx_jaccl_coordinators(
         if n == coordinator:
             return "0.0.0.0"
 
-        ip = _find_ip_prioritised(n, coordinator, cycle_digraph, node_profiles)
+        ip = _find_ip_prioritised(n, coordinator, cycle_digraph, node_network)
         if ip is not None:
             return ip
 

src/exo/master/tests/conftest.py

@@ -2,28 +2,26 @@ from exo.shared.types.multiaddr import Multiaddr
 from exo.shared.types.profiling import (
     MemoryUsage,
     NetworkInterfaceInfo,
-    NodePerformanceProfile,
-    SystemPerformanceProfile,
+    NodeNetworkInfo,
 )
 from exo.shared.types.topology import RDMAConnection, SocketConnection
 
 
-def create_node_profile(memory: int) -> NodePerformanceProfile:
-    return NodePerformanceProfile(
-        model_id="test",
-        chip_id="test",
-        friendly_name="test",
-        memory=MemoryUsage.from_bytes(
-            ram_total=1000,
-            ram_available=memory,
-            swap_total=1000,
-            swap_available=1000,
-        ),
-        network_interfaces=[
+def create_node_memory(memory: int) -> MemoryUsage:
+    return MemoryUsage.from_bytes(
+        ram_total=1000,
+        ram_available=memory,
+        swap_total=1000,
+        swap_available=1000,
+    )
+
+
+def create_node_network() -> NodeNetworkInfo:
+    return NodeNetworkInfo(
+        interfaces=[
             NetworkInterfaceInfo(name="en0", ip_address=f"169.254.0.{i}")
             for i in range(10)
-        ],
-        system=SystemPerformanceProfile(),
+        ]
     )
 
 

src/exo/master/tests/test_master.py

@@ -7,6 +7,7 @@ from loguru import logger
 
 from exo.master.main import Master
 from exo.routing.router import get_node_id_keypair
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.api import ChatCompletionMessage, ChatCompletionTaskParams
 from exo.shared.types.commands import (
     ChatCompletion,
@@ -23,7 +24,6 @@ from exo.shared.types.events import (
     TaskCreated,
 )
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.profiling import (
     MemoryUsage,
 )
@@ -73,8 +73,8 @@ async def test_master():
         tg.start_soon(master.run)
 
         sender_node_id = NodeId(f"{keypair.to_peer_id().to_base58()}_sender")
-        # inject a NodePerformanceProfile event
-        logger.info("inject a NodePerformanceProfile event")
+        # inject a NodeGatheredInfo event
+        logger.info("inject a NodeGatheredInfo event")
         await local_event_sender.send(
             ForwarderEvent(
                 origin_idx=0,
@@ -99,7 +99,7 @@ async def test_master():
         logger.info("wait for initial topology event")
         while len(list(master.state.topology.list_nodes())) == 0:
             await anyio.sleep(0.001)
-        while len(master.state.node_profiles) == 0:
+        while len(master.state.node_memory) == 0:
             await anyio.sleep(0.001)
 
         logger.info("inject a CreateInstance Command")
@@ -109,9 +109,8 @@ async def test_master():
                 command=(
                     PlaceInstance(
                         command_id=CommandId(),
-                        model_meta=ModelMetadata(
+                        model_card=ModelCard(
                             model_id=ModelId("llama-3.2-1b"),
-                            pretty_name="Llama 3.2 1B",
                             n_layers=16,
                             storage_size=Memory.from_bytes(678948),
                             hidden_size=7168,
@@ -167,9 +166,8 @@ async def test_master():
                     start_layer=0,
                     end_layer=16,
                     n_layers=16,
-                    model_meta=ModelMetadata(
+                    model_card=ModelCard(
                         model_id=ModelId("llama-3.2-1b"),
-                        pretty_name="Llama 3.2 1B",
                         n_layers=16,
                         storage_size=Memory.from_bytes(678948),
                         hidden_size=7168,

src/exo/master/tests/test_placement.py

@@ -5,18 +5,19 @@ from exo.master.placement import (
     place_instance,
 )
 from exo.master.tests.conftest import (
-    create_node_profile,
+    create_node_memory,
+    create_node_network,
     create_rdma_connection,
     create_socket_connection,
 )
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.topology import Topology
 from exo.shared.types.commands import PlaceInstance
 from exo.shared.types.common import CommandId, NodeId
 from exo.shared.types.events import InstanceCreated, InstanceDeleted
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.multiaddr import Multiaddr
-from exo.shared.types.profiling import NetworkInterfaceInfo
+from exo.shared.types.profiling import NetworkInterfaceInfo, NodeNetworkInfo
 from exo.shared.types.topology import Connection, SocketConnection
 from exo.shared.types.worker.instances import (
     Instance,
@@ -42,21 +43,20 @@ def instance() -> Instance:
 
 
 @pytest.fixture
-def model_meta() -> ModelMetadata:
-    return ModelMetadata(
+def model_card() -> ModelCard:
+    return ModelCard(
         model_id=ModelId("test-model"),
         storage_size=Memory.from_kb(1000),
-        pretty_name="Test Model",
         n_layers=10,
         hidden_size=30,
         supports_tensor=True,
     )
 
 
-def place_instance_command(model_meta: ModelMetadata) -> PlaceInstance:
+def place_instance_command(model_card: ModelCard) -> PlaceInstance:
     return PlaceInstance(
         command_id=CommandId(),
-        model_meta=model_meta,
+        model_card=model_card,
         sharding=Sharding.Pipeline,
         instance_meta=InstanceMeta.MlxRing,
         min_nodes=1,
@@ -75,16 +75,16 @@ def test_get_instance_placements_create_instance(
     available_memory: tuple[int, int, int],
     total_layers: int,
     expected_layers: tuple[int, int, int],
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
 ):
     # arrange
-    model_meta.n_layers = total_layers
-    model_meta.storage_size.in_bytes = sum(
+    model_card.n_layers = total_layers
+    model_card.storage_size.in_bytes = sum(
         available_memory
     )  # make it exactly fit across all nodes
     topology = Topology()
 
-    cic = place_instance_command(model_meta)
+    cic = place_instance_command(model_card)
     node_id_a = NodeId()
     node_id_b = NodeId()
     node_id_c = NodeId()
@@ -109,10 +109,15 @@ def test_get_instance_placements_create_instance(
         source=node_id_b, sink=node_id_a, edge=create_socket_connection(6)
     )
 
-    profiles = {
-        node_id_a: create_node_profile(available_memory[0]),
-        node_id_b: create_node_profile(available_memory[1]),
-        node_id_c: create_node_profile(available_memory[2]),
+    node_memory = {
+        node_id_a: create_node_memory(available_memory[0]),
+        node_id_b: create_node_memory(available_memory[1]),
+        node_id_c: create_node_memory(available_memory[2]),
+    }
+    node_network = {
+        node_id_a: create_node_network(),
+        node_id_b: create_node_network(),
+        node_id_c: create_node_network(),
     }
     topology.add_node(node_id_a)
     topology.add_node(node_id_b)
@@ -125,13 +130,13 @@ def test_get_instance_placements_create_instance(
     topology.add_connection(conn_b_a)
 
     # act
-    placements = place_instance(cic, topology, {}, profiles)
+    placements = place_instance(cic, topology, {}, node_memory, node_network)
 
     # assert
     assert len(placements) == 1
     instance_id = list(placements.keys())[0]
     instance = placements[instance_id]
-    assert instance.shard_assignments.model_id == model_meta.model_id
+    assert instance.shard_assignments.model_id == model_card.model_id
 
     runner_id_a = instance.shard_assignments.node_to_runner[node_id_a]
     runner_id_b = instance.shard_assignments.node_to_runner[node_id_b]
@@ -155,18 +160,18 @@ def test_get_instance_placements_one_node_exact_fit() -> None:
     topology = Topology()
     node_id = NodeId()
     topology.add_node(node_id)
-    profiles = {node_id: create_node_profile(1000 * 1024)}
+    node_memory = {node_id: create_node_memory(1000 * 1024)}
+    node_network = {node_id: create_node_network()}
     cic = place_instance_command(
-        ModelMetadata(
+        ModelCard(
             model_id=ModelId("test-model"),
             storage_size=Memory.from_kb(1000),
-            pretty_name="Test Model",
             n_layers=10,
             hidden_size=1000,
             supports_tensor=True,
         ),
     )
-    placements = place_instance(cic, topology, {}, profiles)
+    placements = place_instance(cic, topology, {}, node_memory, node_network)
 
     assert len(placements) == 1
     instance_id = list(placements.keys())[0]
@@ -181,18 +186,18 @@ def test_get_instance_placements_one_node_fits_with_extra_memory() -> None:
     topology = Topology()
     node_id = NodeId()
     topology.add_node(node_id)
-    profiles = {node_id: create_node_profile(1001 * 1024)}
+    node_memory = {node_id: create_node_memory(1001 * 1024)}
+    node_network = {node_id: create_node_network()}
     cic = place_instance_command(
-        ModelMetadata(
+        ModelCard(
             model_id=ModelId("test-model"),
             storage_size=Memory.from_kb(1000),
-            pretty_name="Test Model",
             n_layers=10,
             hidden_size=1000,
             supports_tensor=True,
         ),
     )
-    placements = place_instance(cic, topology, {}, profiles)
+    placements = place_instance(cic, topology, {}, node_memory, node_network)
 
     assert len(placements) == 1
     instance_id = list(placements.keys())[0]
@@ -207,12 +212,12 @@ def test_get_instance_placements_one_node_not_fit() -> None:
     topology = Topology()
     node_id = NodeId()
     topology.add_node(node_id)
-    profiles = {node_id: create_node_profile(1000 * 1024)}
+    node_memory = {node_id: create_node_memory(1000 * 1024)}
+    node_network = {node_id: create_node_network()}
     cic = place_instance_command(
-        model_meta=ModelMetadata(
+        model_card=ModelCard(
             model_id=ModelId("test-model"),
             storage_size=Memory.from_kb(1001),
-            pretty_name="Test Model",
             n_layers=10,
             hidden_size=1000,
             supports_tensor=True,
@@ -220,7 +225,7 @@ def test_get_instance_placements_one_node_not_fit() -> None:
     )
 
     with pytest.raises(ValueError, match="No cycles found with sufficient memory"):
-        place_instance(cic, topology, {}, profiles)
+        place_instance(cic, topology, {}, node_memory, node_network)
 
 
 def test_get_transition_events_no_change(instance: Instance):
@@ -266,23 +271,31 @@ def test_get_transition_events_delete_instance(instance: Instance):
 
 
 def test_placement_selects_leaf_nodes(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
 ):
     # arrange
     topology = Topology()
 
-    model_meta.storage_size = Memory.from_bytes(1000)
+    # Model requires more than any single node but fits within a 3-node cycle
+    model_card.storage_size.in_bytes = 1500
+    model_card.n_layers = 12
 
     node_id_a = NodeId()
     node_id_b = NodeId()
     node_id_c = NodeId()
     node_id_d = NodeId()
 
-    profiles = {
-        node_id_a: create_node_profile(500),
-        node_id_b: create_node_profile(600),
-        node_id_c: create_node_profile(600),
-        node_id_d: create_node_profile(500),
+    node_memory = {
+        node_id_a: create_node_memory(500),
+        node_id_b: create_node_memory(600),
+        node_id_c: create_node_memory(600),
+        node_id_d: create_node_memory(500),
+    }
+    node_network = {
+        node_id_a: create_node_network(),
+        node_id_b: create_node_network(),
+        node_id_c: create_node_network(),
+        node_id_d: create_node_network(),
     }
 
     topology.add_node(node_id_a)
@@ -310,10 +323,10 @@ def test_placement_selects_leaf_nodes(
         Connection(source=node_id_d, sink=node_id_c, edge=create_socket_connection(1))
     )
 
-    cic = place_instance_command(model_meta=model_meta)
+    cic = place_instance_command(model_card=model_card)
 
     # act
-    placements = place_instance(cic, topology, {}, profiles)
+    placements = place_instance(cic, topology, {}, node_memory, node_network)
 
     # assert
     assert len(placements) == 1
@@ -329,21 +342,21 @@ def test_placement_selects_leaf_nodes(
 
 
 def test_tensor_rdma_backend_connectivity_matrix(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
 ):
     # arrange
     topology = Topology()
-    model_meta.n_layers = 12
-    model_meta.storage_size.in_bytes = 1500
+    model_card.n_layers = 12
+    model_card.storage_size.in_bytes = 1500
 
     node_a = NodeId()
     node_b = NodeId()
     node_c = NodeId()
 
-    profiles = {
-        node_a: create_node_profile(500),
-        node_b: create_node_profile(500),
-        node_c: create_node_profile(500),
+    node_memory = {
+        node_a: create_node_memory(500),
+        node_b: create_node_memory(500),
+        node_c: create_node_memory(500),
     }
 
     ethernet_interface = NetworkInterfaceInfo(
@@ -354,9 +367,11 @@ def test_tensor_rdma_backend_connectivity_matrix(
         sink_multiaddr=Multiaddr(address="/ip4/10.0.0.1/tcp/8000")
     )
 
-    profiles[node_a].network_interfaces = [ethernet_interface]
-    profiles[node_b].network_interfaces = [ethernet_interface]
-    profiles[node_c].network_interfaces = [ethernet_interface]
+    node_network = {
+        node_a: NodeNetworkInfo(interfaces=[ethernet_interface]),
+        node_b: NodeNetworkInfo(interfaces=[ethernet_interface]),
+        node_c: NodeNetworkInfo(interfaces=[ethernet_interface]),
+    }
 
     topology.add_node(node_a)
     topology.add_node(node_b)
@@ -394,12 +409,12 @@ def test_tensor_rdma_backend_connectivity_matrix(
         sharding=Sharding.Tensor,
         instance_meta=InstanceMeta.MlxJaccl,
         command_id=CommandId(),
-        model_meta=model_meta,
+        model_card=model_card,
         min_nodes=1,
     )
 
     # act
-    placements = place_instance(cic, topology, {}, profiles)
+    placements = place_instance(cic, topology, {}, node_memory, node_network)
 
     # assert
     assert len(placements) == 1

src/exo/master/tests/test_placement_utils.py

@@ -1,5 +1,3 @@
-from copy import copy
-
 import pytest
 
 from exo.master.placement_utils import (
@@ -10,16 +8,17 @@ from exo.master.placement_utils import (
     get_shard_assignments,
     get_smallest_cycles,
 )
-from exo.master.tests.conftest import create_node_profile, create_socket_connection
+from exo.master.tests.conftest import (
+    create_node_memory,
+    create_socket_connection,
+)
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.topology import Topology
 from exo.shared.types.common import Host, NodeId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.profiling import (
-    MemoryUsage,
     NetworkInterfaceInfo,
-    NodePerformanceProfile,
-    SystemPerformanceProfile,
+    NodeNetworkInfo,
 )
 from exo.shared.types.topology import Connection, SocketConnection
 from exo.shared.types.worker.shards import Sharding
@@ -36,9 +35,9 @@ def test_filter_cycles_by_memory():
         source=node2_id, sink=node1_id, edge=create_socket_connection(2)
     )
 
-    node1 = create_node_profile(1000 * 1024)
-    node2 = create_node_profile(1000 * 1024)
-    node_profiles = {node1_id: node1, node2_id: node2}
+    node1_mem = create_node_memory(1000 * 1024)
+    node2_mem = create_node_memory(1000 * 1024)
+    node_memory = {node1_id: node1_mem, node2_id: node2_mem}
 
     topology = Topology()
     topology.add_node(node1_id)
@@ -51,9 +50,7 @@ def test_filter_cycles_by_memory():
     assert len(cycles[0]) == 2
 
     # act
-    filtered_cycles = filter_cycles_by_memory(
-        cycles, node_profiles, Memory.from_bytes(1)
-    )
+    filtered_cycles = filter_cycles_by_memory(cycles, node_memory, Memory.from_bytes(1))
 
     # assert
     assert len(filtered_cycles) == 1
@@ -72,9 +69,9 @@ def test_filter_cycles_by_insufficient_memory():
         source=node2_id, sink=node1_id, edge=create_socket_connection(2)
     )
 
-    node1 = create_node_profile(1000 * 1024)
-    node2 = create_node_profile(1000 * 1024)
-    node_profiles = {node1_id: node1, node2_id: node2}
+    node1_mem = create_node_memory(1000 * 1024)
+    node2_mem = create_node_memory(1000 * 1024)
+    node_memory = {node1_id: node1_mem, node2_id: node2_mem}
 
     topology = Topology()
     topology.add_node(node1_id)
@@ -84,7 +81,7 @@ def test_filter_cycles_by_insufficient_memory():
 
     # act
     filtered_cycles = filter_cycles_by_memory(
-        topology.get_cycles(), node_profiles, Memory.from_kb(2001)
+        topology.get_cycles(), node_memory, Memory.from_kb(2001)
     )
 
     # assert
@@ -109,13 +106,13 @@ def test_filter_multiple_cycles_by_memory():
         source=node_c_id, sink=node_b_id, edge=create_socket_connection(4)
     )
 
-    node_a = create_node_profile(500 * 1024)
-    node_b = create_node_profile(500 * 1024)
-    node_c = create_node_profile(1000 * 1024)
-    node_profiles = {
-        node_a_id: node_a,
-        node_b_id: node_b,
-        node_c_id: node_c,
+    node_a_mem = create_node_memory(500 * 1024)
+    node_b_mem = create_node_memory(500 * 1024)
+    node_c_mem = create_node_memory(1000 * 1024)
+    node_memory = {
+        node_a_id: node_a_mem,
+        node_b_id: node_b_mem,
+        node_c_id: node_c_mem,
     }
 
     topology = Topology()
@@ -130,9 +127,7 @@ def test_filter_multiple_cycles_by_memory():
     cycles = topology.get_cycles()
 
     # act
-    filtered_cycles = filter_cycles_by_memory(
-        cycles, node_profiles, Memory.from_kb(1500)
-    )
+    filtered_cycles = filter_cycles_by_memory(cycles, node_memory, Memory.from_kb(1500))
 
     # assert
     assert len(filtered_cycles) == 1
@@ -228,18 +223,17 @@ def test_get_shard_assignments(
     topology.add_connection(connection3)
     topology.add_connection(connection4)
 
-    node_a = create_node_profile(available_memory[0] * 1024)
-    node_b = create_node_profile(available_memory[1] * 1024)
-    node_c = create_node_profile(available_memory[2] * 1024)
-    node_profiles = {
-        node_a_id: node_a,
-        node_b_id: node_b,
-        node_c_id: node_c,
+    node_a_mem = create_node_memory(available_memory[0] * 1024)
+    node_b_mem = create_node_memory(available_memory[1] * 1024)
+    node_c_mem = create_node_memory(available_memory[2] * 1024)
+    node_memory = {
+        node_a_id: node_a_mem,
+        node_b_id: node_b_mem,
+        node_c_id: node_c_mem,
     }
 
-    model_meta = ModelMetadata(
+    model_card = ModelCard(
         model_id=ModelId("test-model"),
-        pretty_name="Test Model",
         n_layers=total_layers,
         storage_size=Memory.from_kb(1000),
         hidden_size=1000,
@@ -253,7 +247,7 @@ def test_get_shard_assignments(
 
     # act
     shard_assignments = get_shard_assignments(
-        model_meta, selected_cycle, Sharding.Pipeline, node_profiles=node_profiles
+        model_card, selected_cycle, Sharding.Pipeline, node_memory=node_memory
     )
 
     # assert
@@ -343,38 +337,28 @@ def test_get_mlx_jaccl_coordinators():
         source=node_a_id, sink=node_c_id, edge=create_socket_connection(6)
     )
 
-    npp = NodePerformanceProfile(
-        model_id="test",
-        chip_id="test",
-        friendly_name="test",
-        memory=MemoryUsage.from_bytes(
-            ram_total=0,
-            ram_available=0,
-            swap_total=0,
-            swap_available=0,
-        ),
-        network_interfaces=[],
-        system=SystemPerformanceProfile(),
+    network_a = NodeNetworkInfo(
+        interfaces=[
+            NetworkInterfaceInfo(name="en0", ip_address="169.254.0.5"),
+            NetworkInterfaceInfo(name="en0", ip_address="169.254.0.2"),
+        ]
     )
-    npp_a = copy(npp)
-    npp_a.network_interfaces = [
-        NetworkInterfaceInfo(name="en0", ip_address="169.254.0.5"),
-        NetworkInterfaceInfo(name="en0", ip_address="169.254.0.2"),
-    ]
-    npp_b = copy(npp)
-    npp_b.network_interfaces = [
-        NetworkInterfaceInfo(name="en0", ip_address="169.254.0.1"),
-        NetworkInterfaceInfo(name="en0", ip_address="169.254.0.4"),
-    ]
-    npp_c = copy(npp)
-    npp_c.network_interfaces = [
-        NetworkInterfaceInfo(name="en0", ip_address="169.254.0.3"),
-        NetworkInterfaceInfo(name="en0", ip_address="169.254.0.6"),
-    ]
-    node_profiles = {
-        node_a_id: npp_a,
-        node_b_id: npp_b,
-        node_c_id: npp_c,
+    network_b = NodeNetworkInfo(
+        interfaces=[
+            NetworkInterfaceInfo(name="en0", ip_address="169.254.0.1"),
+            NetworkInterfaceInfo(name="en0", ip_address="169.254.0.4"),
+        ]
+    )
+    network_c = NodeNetworkInfo(
+        interfaces=[
+            NetworkInterfaceInfo(name="en0", ip_address="169.254.0.3"),
+            NetworkInterfaceInfo(name="en0", ip_address="169.254.0.6"),
+        ]
+    )
+    node_network = {
+        node_a_id: network_a,
+        node_b_id: network_b,
+        node_c_id: network_c,
     }
 
     topology = Topology()
@@ -394,7 +378,7 @@ def test_get_mlx_jaccl_coordinators():
         node_a_id,
         coordinator_port=5000,
         cycle_digraph=topology,
-        node_profiles=node_profiles,
+        node_network=node_network,
     )
 
     # assert
@@ -496,9 +480,9 @@ def test_get_shard_assignments_insufficient_memory_raises():
     topology = Topology()
 
     # Node C has only 10 KB but would need 50 KB for 1 layer (1000 KB / 20 layers)
-    node_a = create_node_profile(900 * 1024)
-    node_b = create_node_profile(50 * 1024)
-    node_c = create_node_profile(10 * 1024)  # Insufficient memory
+    node_a_mem = create_node_memory(900 * 1024)
+    node_b_mem = create_node_memory(50 * 1024)
+    node_c_mem = create_node_memory(10 * 1024)  # Insufficient memory
 
     topology.add_node(node_a_id)
     topology.add_node(node_b_id)
@@ -521,15 +505,14 @@ def test_get_shard_assignments_insufficient_memory_raises():
     topology.add_connection(conn_c_a)
     topology.add_connection(conn_b_a)
 
-    profiles = {
-        node_a_id: node_a,
-        node_b_id: node_b,
-        node_c_id: node_c,
+    node_memory = {
+        node_a_id: node_a_mem,
+        node_b_id: node_b_mem,
+        node_c_id: node_c_mem,
     }
 
-    model_meta = ModelMetadata(
+    model_card = ModelCard(
         model_id=ModelId("test-model"),
-        pretty_name="Test Model",
         n_layers=20,
         storage_size=Memory.from_kb(1000),
         hidden_size=1000,
@@ -539,4 +522,6 @@ def test_get_shard_assignments_insufficient_memory_raises():
     selected_cycle = cycles[0]
 
     with pytest.raises(ValueError, match="insufficient memory"):
-        get_shard_assignments(model_meta, selected_cycle, Sharding.Pipeline, profiles)
+        get_shard_assignments(
+            model_card, selected_cycle, Sharding.Pipeline, node_memory
+        )

src/exo/master/tests/test_topology.py

@@ -3,11 +3,6 @@ import pytest
 from exo.shared.topology import Topology
 from exo.shared.types.common import NodeId
 from exo.shared.types.multiaddr import Multiaddr
-from exo.shared.types.profiling import (
-    MemoryUsage,
-    NodePerformanceProfile,
-    SystemPerformanceProfile,
-)
 from exo.shared.types.topology import Connection, SocketConnection
 
 
@@ -23,22 +18,6 @@ def socket_connection() -> SocketConnection:
     )
 
 
-@pytest.fixture
-def node_profile() -> NodePerformanceProfile:
-    memory_profile = MemoryUsage.from_bytes(
-        ram_total=1000, ram_available=1000, swap_total=1000, swap_available=1000
-    )
-    system_profile = SystemPerformanceProfile()
-    return NodePerformanceProfile(
-        model_id="test",
-        chip_id="test",
-        friendly_name="test",
-        memory=memory_profile,
-        network_interfaces=[],
-        system=system_profile,
-    )
-
-
 def test_add_node(topology: Topology):
     # arrange
     node_id = NodeId()

src/exo/shared/apply.py

@@ -25,7 +25,11 @@ from exo.shared.types.events import (
     TopologyEdgeCreated,
     TopologyEdgeDeleted,
 )
-from exo.shared.types.profiling import NodePerformanceProfile
+from exo.shared.types.profiling import (
+    NodeIdentity,
+    NodeNetworkInfo,
+    NodeThunderboltInfo,
+)
 from exo.shared.types.state import State
 from exo.shared.types.tasks import Task, TaskId, TaskStatus
 from exo.shared.types.topology import Connection, RDMAConnection
@@ -193,22 +197,43 @@ def apply_runner_deleted(event: RunnerDeleted, state: State) -> State:
 
 def apply_node_timed_out(event: NodeTimedOut, state: State) -> State:
     topology = copy.deepcopy(state.topology)
-    state.topology.remove_node(event.node_id)
-    node_profiles = {
-        key: value for key, value in state.node_profiles.items() if key != event.node_id
-    }
+    topology.remove_node(event.node_id)
     last_seen = {
         key: value for key, value in state.last_seen.items() if key != event.node_id
     }
     downloads = {
         key: value for key, value in state.downloads.items() if key != event.node_id
     }
+    # Clean up all granular node mappings
+    node_identities = {
+        key: value
+        for key, value in state.node_identities.items()
+        if key != event.node_id
+    }
+    node_memory = {
+        key: value for key, value in state.node_memory.items() if key != event.node_id
+    }
+    node_system = {
+        key: value for key, value in state.node_system.items() if key != event.node_id
+    }
+    node_network = {
+        key: value for key, value in state.node_network.items() if key != event.node_id
+    }
+    node_thunderbolt = {
+        key: value
+        for key, value in state.node_thunderbolt.items()
+        if key != event.node_id
+    }
     return state.model_copy(
         update={
             "downloads": downloads,
             "topology": topology,
-            "node_profiles": node_profiles,
             "last_seen": last_seen,
+            "node_identities": node_identities,
+            "node_memory": node_memory,
+            "node_system": node_system,
+            "node_network": node_network,
+            "node_thunderbolt": node_thunderbolt,
         }
     )
 
@@ -217,29 +242,60 @@ def apply_node_gathered_info(event: NodeGatheredInfo, state: State) -> State:
     topology = copy.deepcopy(state.topology)
     topology.add_node(event.node_id)
     info = event.info
-    profile = state.node_profiles.get(event.node_id, NodePerformanceProfile())
+
+    # Build update dict with only the mappings that change
+    update: dict[str, object] = {
+        "last_seen": {
+            **state.last_seen,
+            event.node_id: datetime.fromisoformat(event.when),
+        },
+        "topology": topology,
+    }
+
     match info:
         case MacmonMetrics():
-            profile.system = info.system_profile
-            profile.memory = info.memory
+            update["node_system"] = {
+                **state.node_system,
+                event.node_id: info.system_profile,
+            }
+            update["node_memory"] = {**state.node_memory, event.node_id: info.memory}
         case MemoryUsage():
-            profile.memory = info
+            update["node_memory"] = {**state.node_memory, event.node_id: info}
         case NodeConfig():
             pass
         case MiscData():
-            profile.friendly_name = info.friendly_name
+            current_identity = state.node_identities.get(event.node_id, NodeIdentity())
+            new_identity = current_identity.model_copy(
+                update={"friendly_name": info.friendly_name}
+            )
+            update["node_identities"] = {
+                **state.node_identities,
+                event.node_id: new_identity,
+            }
         case StaticNodeInformation():
-            profile.model_id = info.model
-            profile.chip_id = info.chip
+            current_identity = state.node_identities.get(event.node_id, NodeIdentity())
+            new_identity = current_identity.model_copy(
+                update={"model_id": info.model, "chip_id": info.chip}
+            )
+            update["node_identities"] = {
+                **state.node_identities,
+                event.node_id: new_identity,
+            }
         case NodeNetworkInterfaces():
-            profile.network_interfaces = info.ifaces
+            update["node_network"] = {
+                **state.node_network,
+                event.node_id: NodeNetworkInfo(interfaces=info.ifaces),
+            }
         case MacThunderboltIdentifiers():
-            profile.tb_interfaces = info.idents
+            update["node_thunderbolt"] = {
+                **state.node_thunderbolt,
+                event.node_id: NodeThunderboltInfo(interfaces=info.idents),
+            }
         case MacThunderboltConnections():
             conn_map = {
                 tb_ident.domain_uuid: (nid, tb_ident.rdma_interface)
-                for nid in state.node_profiles
-                for tb_ident in state.node_profiles[nid].tb_interfaces
+                for nid in state.node_thunderbolt
+                for tb_ident in state.node_thunderbolt[nid].interfaces
             }
             as_rdma_conns = [
                 Connection(
@@ -256,15 +312,7 @@ def apply_node_gathered_info(event: NodeGatheredInfo, state: State) -> State:
             ]
             topology.replace_all_out_rdma_connections(event.node_id, as_rdma_conns)
 
-    last_seen = {**state.last_seen, event.node_id: datetime.fromisoformat(event.when)}
-    new_profiles = {**state.node_profiles, event.node_id: profile}
-    return state.model_copy(
-        update={
-            "node_profiles": new_profiles,
-            "last_seen": last_seen,
-            "topology": topology,
-        }
-    )
+    return state.model_copy(update=update)
 
 
 def apply_topology_edge_created(event: TopologyEdgeCreated, state: State) -> State:

src/exo/shared/models/model_cards.py

@@ -1,552 +1,209 @@
-from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
-from exo.utils.pydantic_ext import CamelCaseModel
+from typing import Annotated
 
+import aiofiles
+import aiofiles.os as aios
+import tomlkit
+from anyio import Path, open_file
+from huggingface_hub import model_info
+from loguru import logger
+from pydantic import BaseModel, Field, PositiveInt, ValidationError
+from tomlkit.exceptions import TOMLKitError
+
+from exo.shared.models.model_cards import ModelCard, ModelId
+from exo.shared.types.common import Id
+from exo.shared.types.memory import Memory
+from exo.utils.dashboard_path import RuntimeResources, find_directory
+from exo.utils.pydantic_ext import CamelCaseModel
+from exo.worker.download.download_utils import (
+    ModelSafetensorsIndex,
+    download_file_with_retry,
+    ensure_models_dir,
+)
+
+
+class ModelId(Id):
+    def normalize(self) -> str:
+        return self.replace("/", "--")
+
+    def short(self) -> str:
+        return self.split("/")[-1]
+
+_card_cache: dict[str, ModelCard] = {}
 
 class ModelCard(CamelCaseModel):
-    short_id: str
     model_id: ModelId
-    name: str
-    description: str
-    tags: list[str]
-    metadata: ModelMetadata
+    storage_size: Memory
+    n_layers: PositiveInt
+    hidden_size: PositiveInt
+    supports_tensor: bool
 
+    async def save(self, path: Path) -> None:
+        async with await open_file(path, "w") as f:
+            py = self.model_dump()
+            data = tomlkit.dumps(py)  # pyright: ignore[reportUnknownMemberType]
+            await f.write(data)
+
+    async def save_to_default_path(self) -> None:
+        dir = await find_directory(RuntimeResources.Resources)
+        await self.save(dir / self.model_id.normalize())
+
+    @staticmethod
+    async def load_from_path(path: Path) -> ModelCard:
+        async with await open_file(path, "r") as f:
+            py = tomlkit.loads(await f.read())
+            return ModelCard.model_validate(py)
+
+    @staticmethod
+    async def load_from_default_path(model_id: ModelId) -> ModelCard:
+        return await ModelCard.load_from_path(await find_directory(RuntimeResources.Resources) / model_id.normalize())
+
+    @staticmethod
+    async def load(model_id: ModelId) -> ModelCard:
+        try:
+            return await ModelCard.load_from_default_path(model_id)
+        except (ValidationError, TOMLKitError, FileNotFoundError):
+            return await ModelCard.from_hf(model_id)
+
+
+    @staticmethod
+    async def from_hf(model_id: ModelId) -> ModelCard:
+        """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+        if (mc := _card_cache.get(model_id, None)) is not None:
+            return mc
+        config_data = await get_config_data(model_id)
+        num_layers = config_data.layer_count
+        mem_size_bytes = await get_safetensors_size(model_id)
+
+        mc = ModelCard(
+            model_id=ModelId(model_id),
+            storage_size=mem_size_bytes,
+            n_layers=num_layers,
+            hidden_size=config_data.hidden_size or 0,
+            # TODO: all custom models currently do not support tensor. We could add a dynamic test for this?
+            supports_tensor=False,
+        )
+        _card_cache[model_id] = mc
+        return mc
+
+# TODO: should we cache this? how do we check for changes
+async def get_model_cards() -> list[ModelCard]:
+    dir = await find_directory(RuntimeResources.Resources)
+    cards: list[ModelCard] = []
+    async for file in dir.glob("*.toml"):
+        try:
+            cards.append(await ModelCard.load_from_path(file))
+        except (TOMLKitError, ValidationError):
+            continue
+
+    return cards
 
 MODEL_CARDS: dict[str, ModelCard] = {
-    # deepseek v3
-    "deepseek-v3.1-4bit": ModelCard(
-        short_id="deepseek-v3.1-4bit",
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
-        name="DeepSeek V3.1 (4-bit)",
-        description="""DeepSeek V3.1 is a large language model trained on the DeepSeek V3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
-            pretty_name="DeepSeek V3.1 (4-bit)",
-            storage_size=Memory.from_gb(378),
-            n_layers=61,
-            hidden_size=7168,
-            supports_tensor=True,
-        ),
+    # glm 4.7 flash
+    "glm-4.7-flash-4bit": ModelCard(
+        model_id=ModelId("mlx-community/GLM-4.7-Flash-4bit"),
+        storage_size=Memory.from_gb(18),
+        n_layers=47,
+        hidden_size=2048,
+        supports_tensor=True,
     ),
-    "deepseek-v3.1-8bit": ModelCard(
-        short_id="deepseek-v3.1-8bit",
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-8bit"),
-        name="DeepSeek V3.1 (8-bit)",
-        description="""DeepSeek V3.1 is a large language model trained on the DeepSeek V3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/DeepSeek-V3.1-8bit"),
-            pretty_name="DeepSeek V3.1 (8-bit)",
-            storage_size=Memory.from_gb(713),
-            n_layers=61,
-            hidden_size=7168,
-            supports_tensor=True,
-        ),
+    "glm-4.7-flash-5bit": ModelCard(
+        model_id=ModelId("mlx-community/GLM-4.7-Flash-5bit"),
+        storage_size=Memory.from_gb(21),
+        n_layers=47,
+        hidden_size=2048,
+        supports_tensor=True,
     ),
-    # kimi k2
-    "kimi-k2-instruct-4bit": ModelCard(
-        short_id="kimi-k2-instruct-4bit",
-        model_id=ModelId("mlx-community/Kimi-K2-Instruct-4bit"),
-        name="Kimi K2 Instruct (4-bit)",
-        description="""Kimi K2 is a large language model trained on the Kimi K2 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Kimi-K2-Instruct-4bit"),
-            pretty_name="Kimi K2 Instruct (4-bit)",
-            storage_size=Memory.from_gb(578),
-            n_layers=61,
-            hidden_size=7168,
-            supports_tensor=True,
-        ),
+    "glm-4.7-flash-6bit": ModelCard(
+        model_id=ModelId("mlx-community/GLM-4.7-Flash-6bit"),
+        storage_size=Memory.from_gb(25),
+        n_layers=47,
+        hidden_size=2048,
+        supports_tensor=True,
     ),
-    "kimi-k2-thinking": ModelCard(
-        short_id="kimi-k2-thinking",
-        model_id=ModelId("mlx-community/Kimi-K2-Thinking"),
-        name="Kimi K2 Thinking (4-bit)",
-        description="""Kimi K2 Thinking is the latest, most capable version of open-source thinking model.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Kimi-K2-Thinking"),
-            pretty_name="Kimi K2 Thinking (4-bit)",
-            storage_size=Memory.from_gb(658),
-            n_layers=61,
-            hidden_size=7168,
-            supports_tensor=True,
-        ),
-    ),
-    # llama-3.1
-    "llama-3.1-8b": ModelCard(
-        short_id="llama-3.1-8b",
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"),
-        name="Llama 3.1 8B (4-bit)",
-        description="""Llama 3.1 is a large language model trained on the Llama 3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"),
-            pretty_name="Llama 3.1 8B (4-bit)",
-            storage_size=Memory.from_mb(4423),
-            n_layers=32,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.1-8b-8bit": ModelCard(
-        short_id="llama-3.1-8b-8bit",
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"),
-        name="Llama 3.1 8B (8-bit)",
-        description="""Llama 3.1 is a large language model trained on the Llama 3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"),
-            pretty_name="Llama 3.1 8B (8-bit)",
-            storage_size=Memory.from_mb(8540),
-            n_layers=32,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.1-8b-bf16": ModelCard(
-        short_id="llama-3.1-8b-bf16",
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"),
-        name="Llama 3.1 8B (BF16)",
-        description="""Llama 3.1 is a large language model trained on the Llama 3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"),
-            pretty_name="Llama 3.1 8B (BF16)",
-            storage_size=Memory.from_mb(16100),
-            n_layers=32,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.1-70b": ModelCard(
-        short_id="llama-3.1-70b",
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"),
-        name="Llama 3.1 70B (4-bit)",
-        description="""Llama 3.1 is a large language model trained on the Llama 3.1 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"),
-            pretty_name="Llama 3.1 70B (4-bit)",
-            storage_size=Memory.from_mb(38769),
-            n_layers=80,
-            hidden_size=8192,
-            supports_tensor=True,
-        ),
-    ),
-    # llama-3.2
-    "llama-3.2-1b": ModelCard(
-        short_id="llama-3.2-1b",
-        model_id=ModelId("mlx-community/Llama-3.2-1B-Instruct-4bit"),
-        name="Llama 3.2 1B (4-bit)",
-        description="""Llama 3.2 is a large language model trained on the Llama 3.2 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.2-1B-Instruct-4bit"),
-            pretty_name="Llama 3.2 1B (4-bit)",
-            storage_size=Memory.from_mb(696),
-            n_layers=16,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.2-3b": ModelCard(
-        short_id="llama-3.2-3b",
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-4bit"),
-        name="Llama 3.2 3B (4-bit)",
-        description="""Llama 3.2 is a large language model trained on the Llama 3.2 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-4bit"),
-            pretty_name="Llama 3.2 3B (4-bit)",
-            storage_size=Memory.from_mb(1777),
-            n_layers=28,
-            hidden_size=3072,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.2-3b-8bit": ModelCard(
-        short_id="llama-3.2-3b-8bit",
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-8bit"),
-        name="Llama 3.2 3B (8-bit)",
-        description="""Llama 3.2 is a large language model trained on the Llama 3.2 dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-8bit"),
-            pretty_name="Llama 3.2 3B (8-bit)",
-            storage_size=Memory.from_mb(3339),
-            n_layers=28,
-            hidden_size=3072,
-            supports_tensor=True,
-        ),
-    ),
-    # llama-3.3
-    "llama-3.3-70b": ModelCard(
-        short_id="llama-3.3-70b",
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-4bit"),
-        name="Llama 3.3 70B (4-bit)",
-        description="""The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-4bit"),
-            pretty_name="Llama 3.3 70B",
-            storage_size=Memory.from_mb(38769),
-            n_layers=80,
-            hidden_size=8192,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.3-70b-8bit": ModelCard(
-        short_id="llama-3.3-70b-8bit",
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-8bit"),
-        name="Llama 3.3 70B (8-bit)",
-        description="""The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-8bit"),
-            pretty_name="Llama 3.3 70B (8-bit)",
-            storage_size=Memory.from_mb(73242),
-            n_layers=80,
-            hidden_size=8192,
-            supports_tensor=True,
-        ),
-    ),
-    "llama-3.3-70b-fp16": ModelCard(
-        short_id="llama-3.3-70b-fp16",
-        model_id=ModelId("mlx-community/llama-3.3-70b-instruct-fp16"),
-        name="Llama 3.3 70B (FP16)",
-        description="""The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out)""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/llama-3.3-70b-instruct-fp16"),
-            pretty_name="Llama 3.3 70B (FP16)",
-            storage_size=Memory.from_mb(137695),
-            n_layers=80,
-            hidden_size=8192,
-            supports_tensor=True,
-        ),
-    ),
-    # qwen3
-    "qwen3-0.6b": ModelCard(
-        short_id="qwen3-0.6b",
-        model_id=ModelId("mlx-community/Qwen3-0.6B-4bit"),
-        name="Qwen3 0.6B (4-bit)",
-        description="""Qwen3 0.6B is a large language model trained on the Qwen3 0.6B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-0.6B-4bit"),
-            pretty_name="Qwen3 0.6B (4-bit)",
-            storage_size=Memory.from_mb(327),
-            n_layers=28,
-            hidden_size=1024,
-            supports_tensor=False,
-        ),
-    ),
-    "qwen3-0.6b-8bit": ModelCard(
-        short_id="qwen3-0.6b-8bit",
-        model_id=ModelId("mlx-community/Qwen3-0.6B-8bit"),
-        name="Qwen3 0.6B (8-bit)",
-        description="""Qwen3 0.6B is a large language model trained on the Qwen3 0.6B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-0.6B-8bit"),
-            pretty_name="Qwen3 0.6B (8-bit)",
-            storage_size=Memory.from_mb(666),
-            n_layers=28,
-            hidden_size=1024,
-            supports_tensor=False,
-        ),
-    ),
-    "qwen3-30b": ModelCard(
-        short_id="qwen3-30b",
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-4bit"),
-        name="Qwen3 30B A3B (4-bit)",
-        description="""Qwen3 30B is a large language model trained on the Qwen3 30B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-30B-A3B-4bit"),
-            pretty_name="Qwen3 30B A3B (4-bit)",
-            storage_size=Memory.from_mb(16797),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-30b-8bit": ModelCard(
-        short_id="qwen3-30b-8bit",
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-8bit"),
-        name="Qwen3 30B A3B (8-bit)",
-        description="""Qwen3 30B is a large language model trained on the Qwen3 30B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-30B-A3B-8bit"),
-            pretty_name="Qwen3 30B A3B (8-bit)",
-            storage_size=Memory.from_mb(31738),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-80b-a3B-4bit": ModelCard(
-        short_id="qwen3-80b-a3B-4bit",
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"),
-        name="Qwen3 80B A3B (4-bit)",
-        description="""Qwen3 80B""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"),
-            pretty_name="Qwen3 80B A3B (4-bit)",
-            storage_size=Memory.from_mb(44800),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-80b-a3B-8bit": ModelCard(
-        short_id="qwen3-80b-a3B-8bit",
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"),
-        name="Qwen3 80B A3B (8-bit)",
-        description="""Qwen3 80B""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"),
-            pretty_name="Qwen3 80B A3B (8-bit)",
-            storage_size=Memory.from_mb(84700),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-80b-a3B-thinking-4bit": ModelCard(
-        short_id="qwen3-80b-a3B-thinking-4bit",
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"),
-        name="Qwen3 80B A3B Thinking (4-bit)",
-        description="""Qwen3 80B Reasoning model""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"),
-            pretty_name="Qwen3 80B A3B (4-bit)",
-            storage_size=Memory.from_mb(84700),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-80b-a3B-thinking-8bit": ModelCard(
-        short_id="qwen3-80b-a3B-thinking-8bit",
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"),
-        name="Qwen3 80B A3B Thinking (8-bit)",
-        description="""Qwen3 80B Reasoning model""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"),
-            pretty_name="Qwen3 80B A3B (8-bit)",
-            storage_size=Memory.from_mb(84700),
-            n_layers=48,
-            hidden_size=2048,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-235b-a22b-4bit": ModelCard(
-        short_id="qwen3-235b-a22b-4bit",
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"),
-        name="Qwen3 235B A22B (4-bit)",
-        description="""Qwen3 235B (Active 22B) is a large language model trained on the Qwen3 235B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"),
-            pretty_name="Qwen3 235B A22B (4-bit)",
-            storage_size=Memory.from_gb(132),
-            n_layers=94,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-235b-a22b-8bit": ModelCard(
-        short_id="qwen3-235b-a22b-8bit",
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"),
-        name="Qwen3 235B A22B (8-bit)",
-        description="""Qwen3 235B (Active 22B) is a large language model trained on the Qwen3 235B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"),
-            pretty_name="Qwen3 235B A22B (8-bit)",
-            storage_size=Memory.from_gb(250),
-            n_layers=94,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-coder-480b-a35b-4bit": ModelCard(
-        short_id="qwen3-coder-480b-a35b-4bit",
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"),
-        name="Qwen3 Coder 480B A35B (4-bit)",
-        description="""Qwen3 Coder 480B (Active 35B) is a large language model trained on the Qwen3 Coder 480B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"),
-            pretty_name="Qwen3 Coder 480B A35B (4-bit)",
-            storage_size=Memory.from_gb(270),
-            n_layers=62,
-            hidden_size=6144,
-            supports_tensor=True,
-        ),
-    ),
-    "qwen3-coder-480b-a35b-8bit": ModelCard(
-        short_id="qwen3-coder-480b-a35b-8bit",
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"),
-        name="Qwen3 Coder 480B A35B (8-bit)",
-        description="""Qwen3 Coder 480B (Active 35B) is a large language model trained on the Qwen3 Coder 480B dataset.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"),
-            pretty_name="Qwen3 Coder 480B A35B (8-bit)",
-            storage_size=Memory.from_gb(540),
-            n_layers=62,
-            hidden_size=6144,
-            supports_tensor=True,
-        ),
-    ),
-    # gpt-oss
-    "gpt-oss-120b-MXFP4-Q8": ModelCard(
-        short_id="gpt-oss-120b-MXFP4-Q8",
-        model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-        name="GPT-OSS 120B (MXFP4-Q8, MLX)",
-        description="""OpenAI's GPT-OSS 120B is a 117B-parameter Mixture-of-Experts model designed for high-reasoning and general-purpose use; this variant is a 4-bit MLX conversion for Apple Silicon.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-            pretty_name="GPT-OSS 120B (MXFP4-Q8, MLX)",
-            storage_size=Memory.from_kb(68_996_301),
-            n_layers=36,
-            hidden_size=2880,
-            supports_tensor=True,
-        ),
-    ),
-    "gpt-oss-20b-MXFP4-Q8": ModelCard(
-        short_id="gpt-oss-20b-MXFP4-Q8",
-        model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
-        name="GPT-OSS 20B (MXFP4-Q8, MLX)",
-        description="""OpenAI's GPT-OSS 20B is a medium-sized MoE model for lower-latency and local or specialized use cases; this variant is a 4-bit MLX conversion for Apple Silicon.""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
-            pretty_name="GPT-OSS 20B (MXFP4-Q8, MLX)",
-            storage_size=Memory.from_kb(11_744_051),
-            n_layers=24,
-            hidden_size=2880,
-            supports_tensor=True,
-        ),
-    ),
-    # glm 4.5
-    "glm-4.5-air-8bit": ModelCard(
-        # Needs to be quantized g32 or g16 to work with tensor parallel
-        short_id="glm-4.5-air-8bit",
-        model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
-        name="GLM 4.5 Air 8bit",
-        description="""GLM 4.5 Air 8bit""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
-            pretty_name="GLM 4.5 Air 8bit",
-            storage_size=Memory.from_gb(114),
-            n_layers=46,
-            hidden_size=4096,
-            supports_tensor=False,
-        ),
-    ),
-    "glm-4.5-air-bf16": ModelCard(
-        short_id="glm-4.5-air-bf16",
-        model_id=ModelId("mlx-community/GLM-4.5-Air-bf16"),
-        name="GLM 4.5 Air bf16",
-        description="""GLM 4.5 Air bf16""",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.5-Air-bf16"),
-            pretty_name="GLM 4.5 Air bf16",
-            storage_size=Memory.from_gb(214),
-            n_layers=46,
-            hidden_size=4096,
-            supports_tensor=True,
-        ),
-    ),
-    # glm 4.7
-    "glm-4.7-4bit": ModelCard(
-        short_id="glm-4.7-4bit",
-        model_id=ModelId("mlx-community/GLM-4.7-4bit"),
-        name="GLM 4.7 4bit",
-        description="GLM 4.7 4bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-4bit"),
-            pretty_name="GLM 4.7 4bit",
-            storage_size=Memory.from_bytes(198556925568),
-            n_layers=91,
-            hidden_size=5120,
-            supports_tensor=True,
-        ),
-    ),
-    "glm-4.7-6bit": ModelCard(
-        short_id="glm-4.7-6bit",
-        model_id=ModelId("mlx-community/GLM-4.7-6bit"),
-        name="GLM 4.7 6bit",
-        description="GLM 4.7 6bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-6bit"),
-            pretty_name="GLM 4.7 6bit",
-            storage_size=Memory.from_bytes(286737579648),
-            n_layers=91,
-            hidden_size=5120,
-            supports_tensor=True,
-        ),
-    ),
-    "glm-4.7-8bit-gs32": ModelCard(
-        short_id="glm-4.7-8bit-gs32",
-        model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
-        name="GLM 4.7 8bit (gs32)",
-        description="GLM 4.7 8bit (gs32)",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
-            pretty_name="GLM 4.7 8bit (gs32)",
-            storage_size=Memory.from_bytes(396963397248),
-            n_layers=91,
-            hidden_size=5120,
-            supports_tensor=True,
-        ),
-    ),
-    # minimax-m2
-    "minimax-m2.1-8bit": ModelCard(
-        short_id="minimax-m2.1-8bit",
-        model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
-        name="MiniMax M2.1 8bit",
-        description="MiniMax M2.1 8bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
-            pretty_name="MiniMax M2.1 8bit",
-            storage_size=Memory.from_bytes(242986745856),
-            n_layers=61,
-            hidden_size=3072,
-            supports_tensor=True,
-        ),
-    ),
-    "minimax-m2.1-3bit": ModelCard(
-        short_id="minimax-m2.1-3bit",
-        model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
-        name="MiniMax M2.1 3bit",
-        description="MiniMax M2.1 3bit",
-        tags=[],
-        metadata=ModelMetadata(
-            model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
-            pretty_name="MiniMax M2.1 3bit",
-            storage_size=Memory.from_bytes(100086644736),
-            n_layers=61,
-            hidden_size=3072,
-            supports_tensor=True,
-        ),
+    "glm-4.7-flash-8bit": ModelCard(
+        model_id=ModelId("mlx-community/GLM-4.7-Flash-8bit"),
+        storage_size=Memory.from_gb(32),
+        n_layers=47,
+        hidden_size=2048,
+        supports_tensor=True,
     ),
 }
+
+
+
+
+
+class ConfigData(BaseModel):
+    model_config = {"extra": "ignore"}  # Allow unknown fields
+
+    # Common field names for number of layers across different architectures
+    num_hidden_layers: Annotated[int, Field(ge=0)] | None = None
+    num_layers: Annotated[int, Field(ge=0)] | None = None
+    n_layer: Annotated[int, Field(ge=0)] | None = None
+    n_layers: Annotated[int, Field(ge=0)] | None = None  # Sometimes used
+    num_decoder_layers: Annotated[int, Field(ge=0)] | None = None  # Transformer models
+    decoder_layers: Annotated[int, Field(ge=0)] | None = None  # Some architectures
+    hidden_size: Annotated[int, Field(ge=0)] | None = None
+
+    @property
+    def layer_count(self) -> int:
+        # Check common field names for layer count
+        layer_fields = [
+            self.num_hidden_layers,
+            self.num_layers,
+            self.n_layer,
+            self.n_layers,
+            self.num_decoder_layers,
+            self.decoder_layers,
+        ]
+
+        for layer_count in layer_fields:
+            if layer_count is not None:
+                return layer_count
+
+        raise ValueError(
+            f"No layer count found in config.json: {self.model_dump_json()}"
+        )
+
+
+async def get_config_data(model_id: ModelId) -> ConfigData:
+    """Downloads and parses config.json for a model."""
+    target_dir = (await ensure_models_dir()) / model_id.normalize()
+    await aios.makedirs(target_dir, exist_ok=True)
+    config_path = await download_file_with_retry(
+        str(model_id),
+        "main",
+        "config.json",
+        target_dir,
+        lambda curr_bytes, total_bytes, is_renamed: logger.info(
+            f"Downloading config.json for {model_id}: {curr_bytes}/{total_bytes} ({is_renamed=})"
+        ),
+    )
+    async with aiofiles.open(config_path, "r") as f:
+        return ConfigData.model_validate_json(await f.read())
+
+
+async def get_safetensors_size(model_id: ModelId) -> Memory:
+    """Gets model size from safetensors index or falls back to HF API."""
+    target_dir = (await ensure_models_dir()) / model_id.normalize()
+    await aios.makedirs(target_dir, exist_ok=True)
+    index_path = await download_file_with_retry(
+        str(model_id),
+        "main",
+        "model.safetensors.index.json",
+        target_dir,
+        lambda curr_bytes, total_bytes, is_renamed: logger.info(
+            f"Downloading model.safetensors.index.json for {model_id}: {curr_bytes}/{total_bytes} ({is_renamed=})"
+        ),
+    )
+    async with aiofiles.open(index_path, "r") as f:
+        index_data = ModelSafetensorsIndex.model_validate_json(await f.read())
+
+    metadata = index_data.metadata
+    if metadata is not None:
+        return Memory.from_bytes(metadata.total_size)
+
+    info = model_info(model_id)
+    if info.safetensors is None:
+        raise ValueError(f"No safetensors info found for {model_id}")
+    return Memory.from_bytes(info.safetensors.total)

src/exo/shared/models/model_meta.py

@@ -6,9 +6,8 @@ from huggingface_hub import model_info
 from loguru import logger
 from pydantic import BaseModel, Field
 
-from exo.shared.models.model_cards import MODEL_CARDS
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.worker.download.download_utils import (
     ModelSafetensorsIndex,
     download_file_with_retry,
@@ -91,36 +90,23 @@ async def get_safetensors_size(model_id: str) -> Memory:
         raise ValueError(f"No safetensors info found for {model_id}")
     return Memory.from_bytes(info.safetensors.total)
 
+_model_card_cache: dict[str, ModelCard] = {}
 
-_model_meta_cache: dict[str, ModelMetadata] = {}
-
-
-async def get_model_meta(model_id: str) -> ModelMetadata:
-    if model_id in _model_meta_cache:
-        return _model_meta_cache[model_id]
-    model_meta = await _get_model_meta(model_id)
-    _model_meta_cache[model_id] = model_meta
-    return model_meta
-
-
-async def _get_model_meta(model_id: str) -> ModelMetadata:
+async def get_model_card(model_id: str) -> ModelCard:
     """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+    if model_id in _model_card_cache:
+        return _model_card_cache[model_id]
     config_data = await get_config_data(model_id)
     num_layers = config_data.layer_count
     mem_size_bytes = await get_safetensors_size(model_id)
-    model_card = next(
-        (card for card in MODEL_CARDS.values() if card.model_id == ModelId(model_id)),
-        None,
-    )
 
-    return ModelMetadata(
+    mc = ModelCard(
         model_id=ModelId(model_id),
-        pretty_name=model_card.name if model_card is not None else model_id,
         storage_size=mem_size_bytes,
         n_layers=num_layers,
         hidden_size=config_data.hidden_size or 0,
         # TODO: all custom models currently do not support tensor. We could add a dynamic test for this?
-        supports_tensor=model_card.metadata.supports_tensor
-        if model_card is not None
-        else False,
+        supports_tensor=False,
     )
+    _model_card_cache[model_id] = mc
+    return mc

src/exo/shared/tests/conftest.py

@@ -7,8 +7,8 @@ import pytest
 from _pytest.logging import LogCaptureFixture
 from loguru import logger
 
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.worker.shards import PipelineShardMetadata, ShardMetadata
 
 
@@ -31,9 +31,8 @@ def get_pipeline_shard_metadata(
     model_id: ModelId, device_rank: int, world_size: int = 1
 ) -> ShardMetadata:
     return PipelineShardMetadata(
-        model_meta=ModelMetadata(
+        model_card=ModelCard(
             model_id=model_id,
-            pretty_name=str(model_id),
             storage_size=Memory.from_mb(100000),
             n_layers=32,
             hidden_size=1000,

src/exo/shared/types/api.py

@@ -4,9 +4,9 @@ from typing import Any, Literal
 from pydantic import BaseModel, Field, field_validator
 from pydantic_core import PydanticUseDefault
 
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.common import CommandId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 
@@ -206,7 +206,7 @@ class DeleteInstanceTaskParams(BaseModel):
 class CreateInstanceResponse(BaseModel):
     message: str
     command_id: CommandId
-    model_meta: ModelMetadata
+    model_card: ModelCard
 
 
 class DeleteInstanceResponse(BaseModel):

src/exo/shared/types/chunks.py

@@ -1,10 +1,10 @@
 from enum import Enum
 
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.api import GenerationStats
 from exo.utils.pydantic_ext import TaggedModel
 
 from .api import FinishReason
-from .models import ModelId
 
 
 class ChunkType(str, Enum):

src/exo/shared/types/commands.py

@@ -1,8 +1,8 @@
 from pydantic import Field
 
+from exo.shared.models.model_cards import ModelCard
 from exo.shared.types.api import ChatCompletionTaskParams
 from exo.shared.types.common import CommandId, NodeId
-from exo.shared.types.models import ModelMetadata
 from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.pydantic_ext import CamelCaseModel, TaggedModel
@@ -21,7 +21,7 @@ class ChatCompletion(BaseCommand):
 
 
 class PlaceInstance(BaseCommand):
-    model_meta: ModelMetadata
+    model_card: ModelCard
     sharding: Sharding
     instance_meta: InstanceMeta
     min_nodes: int

src/exo/shared/types/common.py

@@ -16,7 +16,9 @@ class Id(str):
         cls, _source: type, handler: GetCoreSchemaHandler
     ) -> core_schema.CoreSchema:
         # Just use a plain string schema
-        return core_schema.str_schema()
+        return core_schema.no_info_after_validator_function(
+            cls, core_schema.str_schema()
+        )
 
 
 class NodeId(Id):

src/exo/shared/types/models.py

@@ -1,18 +0,0 @@
-from pydantic import PositiveInt
-
-from exo.shared.types.common import Id
-from exo.shared.types.memory import Memory
-from exo.utils.pydantic_ext import CamelCaseModel
-
-
-class ModelId(Id):
-    pass
-
-
-class ModelMetadata(CamelCaseModel):
-    model_id: ModelId
-    pretty_name: str
-    storage_size: Memory
-    n_layers: PositiveInt
-    hidden_size: PositiveInt
-    supports_tensor: bool

src/exo/shared/types/profiling.py

@@ -53,13 +53,21 @@ class NetworkInterfaceInfo(CamelCaseModel):
     ip_address: str
 
 
-class NodePerformanceProfile(CamelCaseModel):
+class NodeIdentity(CamelCaseModel):
+    """Static and slow-changing node identification data."""
+
     model_id: str = "Unknown"
     chip_id: str = "Unknown"
     friendly_name: str = "Unknown"
-    memory: MemoryUsage = MemoryUsage.from_bytes(
-        ram_total=0, ram_available=0, swap_total=0, swap_available=0
-    )
-    network_interfaces: Sequence[NetworkInterfaceInfo] = []
-    tb_interfaces: Sequence[ThunderboltIdentifier] = []
-    system: SystemPerformanceProfile = SystemPerformanceProfile()
+
+
+class NodeNetworkInfo(CamelCaseModel):
+    """Network interface information for a node."""
+
+    interfaces: Sequence[NetworkInterfaceInfo] = []
+
+
+class NodeThunderboltInfo(CamelCaseModel):
+    """Thunderbolt interface identifiers for a node."""
+
+    interfaces: Sequence[ThunderboltIdentifier] = []

src/exo/shared/types/state.py

@@ -7,7 +7,13 @@ from pydantic.alias_generators import to_camel
 
 from exo.shared.topology import Topology, TopologySnapshot
 from exo.shared.types.common import NodeId
-from exo.shared.types.profiling import NodePerformanceProfile
+from exo.shared.types.profiling import (
+    MemoryUsage,
+    NodeIdentity,
+    NodeNetworkInfo,
+    NodeThunderboltInfo,
+    SystemPerformanceProfile,
+)
 from exo.shared.types.tasks import Task, TaskId
 from exo.shared.types.worker.downloads import DownloadProgress
 from exo.shared.types.worker.instances import Instance, InstanceId
@@ -35,11 +41,17 @@ class State(CamelCaseModel):
     runners: Mapping[RunnerId, RunnerStatus] = {}
     downloads: Mapping[NodeId, Sequence[DownloadProgress]] = {}
     tasks: Mapping[TaskId, Task] = {}
-    node_profiles: Mapping[NodeId, NodePerformanceProfile] = {}
     last_seen: Mapping[NodeId, datetime] = {}
     topology: Topology = Field(default_factory=Topology)
     last_event_applied_idx: int = Field(default=-1, ge=-1)
 
+    # Granular node state mappings (update independently at different frequencies)
+    node_identities: Mapping[NodeId, NodeIdentity] = {}
+    node_memory: Mapping[NodeId, MemoryUsage] = {}
+    node_system: Mapping[NodeId, SystemPerformanceProfile] = {}
+    node_network: Mapping[NodeId, NodeNetworkInfo] = {}
+    node_thunderbolt: Mapping[NodeId, NodeThunderboltInfo] = {}
+
     @field_serializer("topology", mode="plain")
     def _encode_topology(self, value: Topology) -> TopologySnapshot:
         return value.to_snapshot()

src/exo/shared/types/worker/runners.py

@@ -2,8 +2,8 @@ from collections.abc import Mapping
 
 from pydantic import model_validator
 
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.common import Id, NodeId
-from exo.shared.types.models import ModelId
 from exo.shared.types.worker.shards import ShardMetadata
 from exo.utils.pydantic_ext import CamelCaseModel, TaggedModel
 

src/exo/shared/types/worker/shards.py

@@ -2,7 +2,7 @@ from enum import Enum
 
 from pydantic import Field
 
-from exo.shared.types.models import ModelMetadata
+from exo.shared.models.model_cards import ModelCard
 from exo.utils.pydantic_ext import TaggedModel
 
 
@@ -17,7 +17,7 @@ class BaseShardMetadata(TaggedModel):
     Replaces previous `Shard` object.
     """
 
-    model_meta: ModelMetadata
+    model_card: ModelCard
     device_rank: int
     world_size: int
 
@@ -41,7 +41,7 @@ class BaseShardMetadata(TaggedModel):
     def __hash__(self) -> int:
         return hash(
             (
-                self.model_meta.model_id,
+                self.model_card.model_id,
                 self.start_layer,
                 self.end_layer,
                 self.n_layers,

src/exo/utils/dashboard_path.py

@@ -1,45 +1,72 @@
+import enum
 import os
 import sys
-from pathlib import Path
 from typing import cast
 
+from anyio import Path
 
-def find_dashboard() -> Path:
-    dashboard = (
-        _find_dashboard_in_env()
-        or _find_dashboard_in_repo()
-        or _find_dashboard_in_bundle()
+
+class RuntimeResources(enum.Enum):
+    Dashboard = enum.auto
+    Resources = enum.auto
+
+_dir_cache: dict[RuntimeResources, Path]
+
+async def find_directory(rr: RuntimeResources) -> Path:
+    dir = (
+        _dir_cache.get(rr, None)
+        or await _find_in_env(rr)
+        or await _find_in_repo(rr)
+        or await _find_in_bundle(rr)
     )
-    if not dashboard:
+    if not dir:
         raise FileNotFoundError(
-            "Unable to locate dashboard assets - make sure the dashboard has been built, or export DASHBOARD_DIR if you've built the dashboard elsewhere."
+            "Unable to locate directory - make sure the dashboard has been built and the runtime resources (model cards) exist."
         )
-    return dashboard
+    _dir_cache[rr] = dir
+    return dir
 
 
-def _find_dashboard_in_env() -> Path | None:
-    env = os.environ.get("DASHBOARD_DIR")
+async def _find_in_env(rr: RuntimeResources) -> Path | None:
+    match rr:
+        case RuntimeResources.Dashboard:
+            env = os.environ.get("DASHBOARD_DIR")
+        case RuntimeResources.Resources:
+            env = os.environ.get("RESOURCES_DIR")
     if not env:
         return None
-    resolved_env = Path(env).expanduser().resolve()
+    resolved_env = await (await Path(env).expanduser()).resolve()
 
     return resolved_env
 
 
-def _find_dashboard_in_repo() -> Path | None:
-    current_module = Path(__file__).resolve()
+async def _find_in_repo(rr: RuntimeResources) -> Path | None:
+    current_module = await Path(__file__).resolve()
     for parent in current_module.parents:
-        build = parent / "dashboard" / "build"
-        if build.is_dir() and (build / "index.html").exists():
-            return build
+        match rr:
+            case RuntimeResources.Dashboard:
+                build = parent / "dashboard" / "build"
+                if await build.is_dir() and await (build / "index.html").exists():
+                    return build
+            case RuntimeResources.Resources:
+                res = parent / "resources"
+                if await res.is_dir():
+                    return res
     return None
 
 
-def _find_dashboard_in_bundle() -> Path | None:
+async def _find_in_bundle(rr: RuntimeResources) -> Path | None:
     frozen_root = cast(str | None, getattr(sys, "_MEIPASS", None))
     if frozen_root is None:
         return None
-    candidate = Path(frozen_root) / "dashboard"
-    if candidate.is_dir():
-        return candidate
+
+    match rr:
+        case RuntimeResources.Dashboard:
+            candidate = Path(frozen_root) / "dashboard"
+            if await candidate.is_dir():
+                return candidate
+        case RuntimeResources.Resources:
+            candidate = Path(frozen_root) / "resources"
+            if await candidate.is_dir():
+                return candidate
     return None

src/exo/utils/info_gatherer/net_profile.py

@@ -7,7 +7,7 @@ from loguru import logger
 
 from exo.shared.topology import Topology
 from exo.shared.types.common import NodeId
-from exo.shared.types.profiling import NodePerformanceProfile
+from exo.shared.types.profiling import NodeNetworkInfo
 
 REACHABILITY_ATTEMPTS = 3
 
@@ -79,7 +79,7 @@ async def check_reachability(
 async def check_reachable(
     topology: Topology,
     self_node_id: NodeId,
-    node_profiles: Mapping[NodeId, NodePerformanceProfile],
+    node_network: Mapping[NodeId, NodeNetworkInfo],
 ) -> dict[NodeId, set[str]]:
     """Check which nodes are reachable and return their IPs."""
 
@@ -98,11 +98,11 @@ async def check_reachable(
         create_task_group() as tg,
     ):
         for node_id in topology.list_nodes():
-            if node_id not in node_profiles:
+            if node_id not in node_network:
                 continue
             if node_id == self_node_id:
                 continue
-            for iface in node_profiles[node_id].network_interfaces:
+            for iface in node_network[node_id].interfaces:
                 tg.start_soon(
                     check_reachability,
                     iface.ip_address,

src/exo/worker/download/download_utils.py

@@ -460,10 +460,10 @@ async def resolve_allow_patterns(shard: ShardMetadata) -> list[str]:
     # (iii) Tensor parallel requires all files.
     return ["*"]
     try:
-        weight_map = await get_weight_map(str(shard.model_meta.model_id))
+        weight_map = await get_weight_map(str(shard.model_card.model_id))
         return get_allow_patterns(weight_map, shard)
     except Exception:
-        logger.error(f"Error getting weight map for {shard.model_meta.model_id=}")
+        logger.error(f"Error getting weight map for {shard.model_card.model_id=}")
         logger.error(traceback.format_exc())
         return ["*"]
 
@@ -532,18 +532,18 @@ async def download_shard(
     allow_patterns: list[str] | None = None,
 ) -> tuple[Path, RepoDownloadProgress]:
     if not skip_download:
-        logger.info(f"Downloading {shard.model_meta.model_id=}")
+        logger.info(f"Downloading {shard.model_card.model_id=}")
 
     # Handle local paths
-    if await aios.path.exists(str(shard.model_meta.model_id)):
-        logger.info(f"Using local model path {shard.model_meta.model_id}")
-        local_path = Path(str(shard.model_meta.model_id))
+    if await aios.path.exists(str(shard.model_card.model_id)):
+        logger.info(f"Using local model path {shard.model_card.model_id}")
+        local_path = Path(str(shard.model_card.model_id))
         return local_path, await download_progress_for_local_path(
-            str(shard.model_meta.model_id), shard, local_path
+            str(shard.model_card.model_id), shard, local_path
         )
 
     revision = "main"
-    target_dir = await ensure_models_dir() / str(shard.model_meta.model_id).replace(
+    target_dir = await ensure_models_dir() / str(shard.model_card.model_id).replace(
         "/", "--"
     )
     if not skip_download:
@@ -552,13 +552,13 @@ async def download_shard(
     if not allow_patterns:
         allow_patterns = await resolve_allow_patterns(shard)
 
-    logger.info(f"Downloading {shard.model_meta.model_id=} with {allow_patterns=}")
+    logger.info(f"Downloading {shard.model_card.model_id=} with {allow_patterns=}")
 
     all_start_time = time.time()
     # TODO: currently not recursive. Some models might require subdirectories - thus this will need to be changed.
     #  Update: <- This does not seem to be the case. Yay?
     file_list = await fetch_file_list_with_cache(
-        str(shard.model_meta.model_id), revision, recursive=True
+        str(shard.model_card.model_id), revision, recursive=True
     )
     filtered_file_list = list(
         filter_repo_objects(
@@ -592,7 +592,7 @@ async def download_shard(
             else timedelta(seconds=0)
         )
         file_progress[file.path] = RepoFileDownloadProgress(
-            repo_id=str(shard.model_meta.model_id),
+            repo_id=str(shard.model_card.model_id),
             repo_revision=revision,
             file_path=file.path,
             downloaded=Memory.from_bytes(curr_bytes),
@@ -609,7 +609,7 @@ async def download_shard(
             shard,
             calculate_repo_progress(
                 shard,
-                str(shard.model_meta.model_id),
+                str(shard.model_card.model_id),
                 revision,
                 file_progress,
                 all_start_time,
@@ -619,7 +619,7 @@ async def download_shard(
     for file in filtered_file_list:
         downloaded_bytes = await get_downloaded_size(target_dir / file.path)
         file_progress[file.path] = RepoFileDownloadProgress(
-            repo_id=str(shard.model_meta.model_id),
+            repo_id=str(shard.model_card.model_id),
             repo_revision=revision,
             file_path=file.path,
             downloaded=Memory.from_bytes(downloaded_bytes),
@@ -643,7 +643,7 @@ async def download_shard(
     async def download_with_semaphore(file: FileListEntry) -> None:
         async with semaphore:
             await download_file_with_retry(
-                str(shard.model_meta.model_id),
+                str(shard.model_card.model_id),
                 revision,
                 file.path,
                 target_dir,
@@ -657,7 +657,7 @@ async def download_shard(
             *[download_with_semaphore(file) for file in filtered_file_list]
         )
     final_repo_progress = calculate_repo_progress(
-        shard, str(shard.model_meta.model_id), revision, file_progress, all_start_time
+        shard, str(shard.model_card.model_id), revision, file_progress, all_start_time
     )
     await on_progress(shard, final_repo_progress)
     if gguf := next((f for f in filtered_file_list if f.path.endswith(".gguf")), None):

src/exo/worker/download/impl_shard_downloader.py

@@ -3,8 +3,7 @@ from collections.abc import Awaitable
 from pathlib import Path
 from typing import AsyncIterator, Callable
 
-from exo.shared.models.model_cards import MODEL_CARDS
-from exo.shared.models.model_meta import get_model_meta
+from exo.shared.models.model_cards import ModelCard, get_model_cards
 from exo.shared.types.worker.shards import (
     PipelineShardMetadata,
     ShardMetadata,
@@ -20,21 +19,21 @@ def exo_shard_downloader(max_parallel_downloads: int = 8) -> ShardDownloader:
 
 
 async def build_base_shard(model_id: str) -> ShardMetadata:
-    model_meta = await get_model_meta(model_id)
+    model_card = await ModelCard.from_hf(model_id)
     return PipelineShardMetadata(
-        model_meta=model_meta,
+        model_card=model_card,
         device_rank=0,
         world_size=1,
         start_layer=0,
-        end_layer=model_meta.n_layers,
-        n_layers=model_meta.n_layers,
+        end_layer=model_card.n_layers,
+        n_layers=model_card.n_layers,
     )
 
 
 async def build_full_shard(model_id: str) -> PipelineShardMetadata:
     base_shard = await build_base_shard(model_id)
     return PipelineShardMetadata(
-        model_meta=base_shard.model_meta,
+        model_card=base_shard.model_card,
         device_rank=base_shard.device_rank,
         world_size=base_shard.world_size,
         start_layer=base_shard.start_layer,
@@ -93,11 +92,11 @@ class CachedShardDownloader(ShardDownloader):
     async def ensure_shard(
         self, shard: ShardMetadata, config_only: bool = False
     ) -> Path:
-        if (shard.model_meta.model_id, shard) in self.cache:
-            return self.cache[(shard.model_meta.model_id, shard)]
+        if (shard.model_card.model_id, shard) in self.cache:
+            return self.cache[(shard.model_card.model_id, shard)]
 
         target_dir = await self.shard_downloader.ensure_shard(shard, config_only)
-        self.cache[(shard.model_meta.model_id, shard)] = target_dir
+        self.cache[(shard.model_card.model_id, shard)] = target_dir
         return target_dir
 
     async def get_shard_download_status(
@@ -159,7 +158,7 @@ class ResumableShardDownloader(ShardDownloader):
         # Kick off download status coroutines concurrently
         tasks = [
             asyncio.create_task(_status_for_model(model_card.model_id))
-            for model_card in MODEL_CARDS.values()
+            for model_card in await get_model_cards()
         ]
 
         for task in asyncio.as_completed(tasks):

src/exo/worker/download/shard_downloader.py

@@ -5,8 +5,8 @@ from datetime import timedelta
 from pathlib import Path
 from typing import AsyncIterator, Callable
 
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.worker.shards import (
     PipelineShardMetadata,
     ShardMetadata,
@@ -86,9 +86,8 @@ NOOP_DOWNLOAD_PROGRESS = RepoDownloadProgress(
     repo_id="noop",
     repo_revision="noop",
     shard=PipelineShardMetadata(
-        model_meta=ModelMetadata(
+        model_card=ModelCard(
             model_id=ModelId("noop"),
-            pretty_name="noope",
             storage_size=Memory.from_bytes(0),
             n_layers=1,
             hidden_size=1,

src/exo/worker/engines/mlx/__init__.py

@@ -1,5 +1,3 @@
-from typing import Any
-
 import mlx.core as mx
 import mlx.nn as nn
 from mlx_lm.models.cache import KVCache
@@ -17,27 +15,3 @@ class Model(nn.Module):
         cache: list[KVCache] | None,
         input_embeddings: mx.array | None = None,
     ) -> mx.array: ...
-
-
-class Detokenizer:
-    def reset(self) -> None: ...
-    def add_token(self, token: int) -> None: ...
-    def finalize(self) -> None: ...
-
-    @property
-    def last_segment(self) -> str: ...
-
-
-class TokenizerWrapper:
-    bos_token: str | None
-    eos_token_ids: list[int]
-    detokenizer: Detokenizer
-
-    def encode(self, text: str, add_special_tokens: bool = True) -> list[int]: ...
-
-    def apply_chat_template(
-        self,
-        messages_dicts: list[dict[str, Any]],
-        tokenize: bool = False,
-        add_generation_prompt: bool = True,
-    ) -> str: ...

src/exo/worker/engines/mlx/auto_parallel.py

@@ -1,7 +1,10 @@
+import os
+import threading
 from abc import ABC, abstractmethod
+from collections.abc import Callable
 from functools import partial
 from inspect import signature
-from typing import TYPE_CHECKING, Callable, Protocol, cast
+from typing import TYPE_CHECKING, Any, Protocol, cast
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -29,6 +32,40 @@ from mlx_lm.models.qwen3_next import Qwen3NextSparseMoeBlock
 from exo.shared.logging import logger
 from exo.shared.types.worker.shards import PipelineShardMetadata
 
+TimeoutCallback = Callable[[], None]
+
+
+def eval_with_timeout(
+    mlx_item: Any,  # pyright: ignore[reportAny]
+    timeout_seconds: float = 60.0,
+    on_timeout: TimeoutCallback | None = None,
+) -> None:
+    """Evaluate MLX item with a hard timeout.
+
+    If on_timeout callback is provided, it will be called before terminating
+    the process. This allows the runner to send a failure event before exit.
+    """
+    completed = threading.Event()
+
+    def watchdog() -> None:
+        if not completed.wait(timeout=timeout_seconds):
+            logger.error(
+                f"mlx_item evaluation timed out after {timeout_seconds:.0f}s. "
+                "This may indicate an issue with FAST_SYNCH and tensor parallel sharding. "
+                "Terminating process."
+            )
+            if on_timeout is not None:
+                on_timeout()
+            os._exit(1)
+
+    watchdog_thread = threading.Thread(target=watchdog, daemon=True)
+    watchdog_thread.start()
+
+    try:
+        mx.eval(mlx_item)  # pyright: ignore[reportAny]
+    finally:
+        completed.set()
+
 
 class _LayerCallable(Protocol):
     """Structural type that any compatible layer must satisfy.
@@ -225,9 +262,37 @@ def patch_pipeline_model[T](model: T, group: mx.distributed.Group) -> T:
     return model
 
 
+def patch_tensor_model[T](model: T) -> T:
+    """Patch model's __call__ to ensure distributed ops sync during inference."""
+    cls = model.__class__
+    original_call = cls.__call__
+    call_signature = signature(original_call)
+
+    def patched_call(
+        self: T,
+        *args: object,
+        **kwargs: object,
+    ) -> mx.array:
+        logits: mx.array = original_call(self, *args, **kwargs)  # pyright: ignore[reportAny]
+        cache = call_signature.bind_partial(self, *args, **kwargs).arguments.get(
+            "cache", None
+        )
+
+        # Add dependency to last cache entry to ensure distributed ops are evaluated
+        if cache is not None and len(cache) > 0:  # pyright: ignore[reportAny]
+            cache[-1].state = mx.depends(cache[-1].state, logits)  # pyright: ignore[reportAny,reportUnknownMemberType]
+
+        return logits
+
+    cls.__call__ = patched_call
+    return model
+
+
 def tensor_auto_parallel(
     model: nn.Module,
     group: mx.distributed.Group,
+    timeout_seconds: float = 60.0,
+    on_timeout: TimeoutCallback | None = None,
 ) -> nn.Module:
     all_to_sharded_linear = partial(
         shard_linear,
@@ -272,7 +337,7 @@ def tensor_auto_parallel(
     if hasattr(model, "shard"):
         try:
             model.shard(group)  # type: ignore
-            return model
+            return patch_tensor_model(model)
         except (AttributeError, TypeError, NameError):
             pass
 
@@ -322,7 +387,10 @@ def tensor_auto_parallel(
     else:
         raise ValueError(f"Unsupported model type: {type(model)}")
 
-    return tensor_parallel_sharding_strategy.shard_model(model)
+    model = tensor_parallel_sharding_strategy.shard_model(
+        model, timeout_seconds, on_timeout
+    )
+    return patch_tensor_model(model)
 
 
 class TensorParallelShardingStrategy(ABC):
@@ -342,13 +410,27 @@ class TensorParallelShardingStrategy(ABC):
         self.N = group.size()
 
     @abstractmethod
-    def shard_model(self, model: nn.Module) -> nn.Module: ...
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module: ...
 
 
 class LlamaShardingStrategy(TensorParallelShardingStrategy):
-    def shard_model(self, model: nn.Module) -> nn.Module:
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module:
         model = cast(LlamaModel, model)
         for layer in model.layers:
+            # Force load weights before sharding to avoid FAST_SYNCH deadlock
+            eval_with_timeout(
+                layer.parameters(), timeout_seconds / len(model.layers), on_timeout
+            )
             layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
             layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
             layer.self_attn.v_proj = self.all_to_sharded_linear(layer.self_attn.v_proj)
@@ -391,9 +473,17 @@ def _set_layers(model: nn.Module, layers: list[_LayerCallable]) -> None:
 
 
 class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
-    def shard_model(self, model: nn.Module) -> nn.Module:
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module:
         model = cast(DeepseekV3Model, model)
         for layer in model.layers:
+            eval_with_timeout(
+                layer.parameters(), timeout_seconds / len(model.layers), on_timeout
+            )
             # Shard the self attention
             if layer.self_attn.q_lora_rank is None:
                 layer.self_attn.q_proj = self.all_to_sharded_linear(
@@ -445,9 +535,17 @@ class ShardedDeepseekV3MoE(CustomMlxLayer):
 
 
 class MiniMaxShardingStrategy(TensorParallelShardingStrategy):
-    def shard_model(self, model: nn.Module) -> nn.Module:
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module:
         model = cast(MiniMaxModel, model)
         for layer in model.layers:
+            eval_with_timeout(
+                layer.parameters(), timeout_seconds / len(model.layers), on_timeout
+            )
             # Shard the self attention
             layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
             layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
@@ -468,15 +566,23 @@ class MiniMaxShardingStrategy(TensorParallelShardingStrategy):
                 layer.block_sparse_moe.switch_mlp.up_proj
             )
             layer.block_sparse_moe = ShardedQwenMoE(layer.block_sparse_moe)  # pyright: ignore[reportAttributeAccessIssue, reportArgumentType]
-            layer.block_sparse_moe.sharding_group = self.group
+            layer.block_sparse_moe.sharding_group = self.group  # pyright: ignore[reportAttributeAccessIssue]
 
         return model
 
 
 class QwenShardingStrategy(TensorParallelShardingStrategy):
-    def shard_model(self, model: nn.Module) -> nn.Module:
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module:
         model = cast(Qwen3MoeModel, model)
         for layer in model.layers:
+            eval_with_timeout(
+                layer.parameters(), timeout_seconds / len(model.layers), on_timeout
+            )
             # Shard the self attention
             layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
             layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
@@ -520,10 +626,18 @@ class ShardedQwenMoE(CustomMlxLayer):
 
 
 class GptOssShardingStrategy(TensorParallelShardingStrategy):
-    def shard_model(self, model: nn.Module) -> nn.Module:
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module:
         model = cast(GptOssMoeModel, model)
 
         for layer in model.layers:
+            eval_with_timeout(
+                layer.parameters(), timeout_seconds / len(model.layers), on_timeout
+            )
             layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
             layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
             layer.self_attn.v_proj = self.all_to_sharded_linear(layer.self_attn.v_proj)
@@ -547,7 +661,7 @@ class GptOssShardingStrategy(TensorParallelShardingStrategy):
             self.all_to_sharded_linear_in_place(layer.mlp.experts.up_proj)
 
             layer.mlp = ShardedGptOssMoE(layer.mlp)  # type: ignore
-            layer.mlp.sharding_group = self.group
+            layer.mlp.sharding_group = self.group  # pyright: ignore[reportAttributeAccessIssue]
 
         return model
 

src/exo/worker/engines/mlx/generator/generate.py

@@ -119,6 +119,7 @@ def mlx_generate(
     model: Model,
     tokenizer: TokenizerWrapper,
     task: ChatCompletionTaskParams,
+    prompt: str,
 ) -> Generator[GenerationResponse]:
     # Ensure that generation stats only contains peak memory for this generation
     mx.reset_peak_memory()
@@ -130,11 +131,6 @@ def mlx_generate(
     if task.seed is not None:
         mx.random.seed(task.seed)
 
-    prompt = apply_chat_template(
-        tokenizer=tokenizer,
-        chat_task_data=task,
-    )
-
     caches = make_kv_cache(model=model)
 
     logits_processors: list[Callable[[mx.array, mx.array], mx.array]] = []

src/exo/worker/engines/mlx/utils_mlx.py

@@ -2,9 +2,7 @@ import json
 import os
 import resource
 import sys
-import threading
 import time
-from collections.abc import Callable
 from pathlib import Path
 from typing import Any, cast
 
@@ -59,6 +57,8 @@ from exo.shared.types.worker.shards import (
 from exo.worker.download.download_utils import build_model_path
 from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.auto_parallel import (
+    TimeoutCallback,
+    eval_with_timeout,
     pipeline_auto_parallel,
     tensor_auto_parallel,
 )
@@ -75,7 +75,7 @@ def get_weights_size(model_shard_meta: ShardMetadata) -> Memory:
     return Memory.from_float_kb(
         (model_shard_meta.end_layer - model_shard_meta.start_layer)
         / model_shard_meta.n_layers
-        * model_shard_meta.model_meta.storage_size.in_kb
+        * model_shard_meta.model_card.storage_size.in_kb
         / (
             1
             if isinstance(model_shard_meta, PipelineShardMetadata)
@@ -88,41 +88,6 @@ class ModelLoadingTimeoutError(Exception):
     pass
 
 
-TimeoutCallback = Callable[[], None]
-
-
-def eval_with_timeout(
-    mlx_item: Any,  # pyright: ignore[reportAny]
-    timeout_seconds: float = 60.0,
-    on_timeout: TimeoutCallback | None = None,
-) -> None:
-    """Evaluate MLX item with a hard timeout.
-
-    If on_timeout callback is provided, it will be called before terminating
-    the process. This allows the runner to send a failure event before exit.
-    """
-    completed = threading.Event()
-
-    def watchdog() -> None:
-        if not completed.wait(timeout=timeout_seconds):
-            logger.error(
-                f"mlx_item evaluation timed out after {timeout_seconds:.0f}s. "
-                "This may indicate an issue with FAST_SYNCH and tensor parallel sharding. "
-                "Terminating process."
-            )
-            if on_timeout is not None:
-                on_timeout()
-            os._exit(1)
-
-    watchdog_thread = threading.Thread(target=watchdog, daemon=True)
-    watchdog_thread.start()
-
-    try:
-        mx.eval(mlx_item)  # pyright: ignore[reportAny]
-    finally:
-        completed.set()
-
-
 def mx_barrier(group: Group | None = None):
     mx.eval(
         mx.distributed.all_sum(
@@ -204,10 +169,10 @@ def mlx_distributed_init(
 
                 # TODO: update once upstream fixes
                 logger.info(
-                    f"rank {rank} MLX_IBV_DEVICES: {coordination_file} with devices: {jaccl_devices_json}"
+                    f"rank {rank} MLX_JACCL_DEVICES: {coordination_file} with devices: {jaccl_devices_json}"
                 )
                 logger.info(f"rank {rank} MLX_JACCL_COORDINATOR: {jaccl_coordinator}")
-                os.environ["MLX_IBV_DEVICES"] = coordination_file
+                os.environ["MLX_JACCL_DEVICES"] = coordination_file
                 os.environ["MLX_RANK"] = str(rank)
                 os.environ["MLX_JACCL_COORDINATOR"] = jaccl_coordinator
                 group = mx.distributed.init(backend="jaccl", strict=True)
@@ -241,7 +206,7 @@ def load_mlx_items(
 ) -> tuple[Model, TokenizerWrapper]:
     if group is None:
         logger.info(f"Single device used for {bound_instance.instance}")
-        model_path = build_model_path(bound_instance.bound_shard.model_meta.model_id)
+        model_path = build_model_path(bound_instance.bound_shard.model_card.model_id)
         start_time = time.perf_counter()
         model, _ = load_model(model_path, strict=True)
         end_time = time.perf_counter()
@@ -269,7 +234,7 @@ def shard_and_load(
     group: Group,
     on_timeout: TimeoutCallback | None = None,
 ) -> tuple[nn.Module, TokenizerWrapper]:
-    model_path = build_model_path(shard_metadata.model_meta.model_id)
+    model_path = build_model_path(shard_metadata.model_card.model_id)
 
     model, _ = load_model(model_path, lazy=True, strict=False)
     logger.debug(model)
@@ -296,14 +261,6 @@ def shard_and_load(
 
     logger.info(f"Group size: {group.size()}, group rank: {group.rank()}")
 
-    match shard_metadata:
-        case TensorShardMetadata():
-            logger.info(f"loading model from {model_path} with tensor parallelism")
-            model = tensor_auto_parallel(model, group)
-        case PipelineShardMetadata():
-            logger.info(f"loading model from {model_path} with pipeline parallelism")
-            model = pipeline_auto_parallel(model, group, shard_metadata)
-
     # Estimate timeout based on model size
     base_timeout = float(os.environ.get("EXO_MODEL_LOAD_TIMEOUT", "60"))
     model_size_gb = get_weights_size(shard_metadata).in_bytes / (1024**3)
@@ -312,7 +269,15 @@ def shard_and_load(
         f"Evaluating model parameters with timeout of {timeout_seconds:.0f}s "
         f"(model size: {model_size_gb:.1f}GB)"
     )
-    eval_with_timeout(model.parameters(), timeout_seconds, on_timeout)
+
+    match shard_metadata:
+        case TensorShardMetadata():
+            logger.info(f"loading model from {model_path} with tensor parallelism")
+            model = tensor_auto_parallel(model, group, timeout_seconds, on_timeout)
+        case PipelineShardMetadata():
+            logger.info(f"loading model from {model_path} with pipeline parallelism")
+            model = pipeline_auto_parallel(model, group, shard_metadata)
+            eval_with_timeout(model.parameters(), timeout_seconds, on_timeout)
 
     # TODO: Do we need this?
     mx.eval(model)
@@ -328,7 +293,7 @@ def shard_and_load(
 
 def get_tokenizer(model_path: Path, shard_metadata: ShardMetadata) -> TokenizerWrapper:
     """Load tokenizer for a model shard. Delegates to load_tokenizer_for_model_id."""
-    return load_tokenizer_for_model_id(shard_metadata.model_meta.model_id, model_path)
+    return load_tokenizer_for_model_id(shard_metadata.model_card.model_id, model_path)
 
 
 def get_eos_token_ids_for_model(model_id: str) -> list[int] | None:
@@ -347,6 +312,9 @@ def get_eos_token_ids_for_model(model_id: str) -> list[int] | None:
     model_id_lower = model_id.lower()
     if "kimi-k2" in model_id_lower:
         return [163586]
+    elif "glm-4.7-flash" in model_id_lower:
+        # 154820: <|endoftext|>, 154827: <|user|>, 154829: <|observation|>
+        return [154820, 154827, 154829]
     elif "glm" in model_id_lower:
         return [151336, 151329, 151338]
     return None
@@ -431,6 +399,16 @@ def apply_chat_template(
     return prompt
 
 
+def detect_thinking_prompt_suffix(prompt: str, tokenizer: TokenizerWrapper) -> bool:
+    """
+    Detect if prompt ends with a thinking opening tag that should be
+    prepended to the output stream.
+    """
+    think_token = tokenizer.think_start
+
+    return think_token is not None and prompt.rstrip().endswith(think_token)
+
+
 class NullKVCache(KVCache):
     """
     A KVCache that pretends to exist but holds zero tokens.

src/exo/worker/main.py

@@ -8,6 +8,7 @@ from loguru import logger
 
 from exo.routing.connection_message import ConnectionMessage, ConnectionMessageType
 from exo.shared.apply import apply
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.commands import ForwarderCommand, RequestEventLog
 from exo.shared.types.common import NodeId, SessionId
 from exo.shared.types.events import (
@@ -22,7 +23,6 @@ from exo.shared.types.events import (
     TopologyEdgeCreated,
     TopologyEdgeDeleted,
 )
-from exo.shared.types.models import ModelId
 from exo.shared.types.multiaddr import Multiaddr
 from exo.shared.types.state import State
 from exo.shared.types.tasks import (
@@ -186,11 +186,11 @@ class Worker:
                         )
                     )
                 case DownloadModel(shard_metadata=shard):
-                    if shard.model_meta.model_id not in self.download_status:
+                    if shard.model_card.model_id not in self.download_status:
                         progress = DownloadPending(
                             shard_metadata=shard, node_id=self.node_id
                         )
-                        self.download_status[shard.model_meta.model_id] = progress
+                        self.download_status[shard.model_card.model_id] = progress
                         await self.event_sender.send(
                             NodeDownloadProgress(download_progress=progress)
                         )
@@ -205,7 +205,7 @@ class Worker:
                             node_id=self.node_id,
                             total_bytes=initial_progress.total_bytes,
                         )
-                        self.download_status[shard.model_meta.model_id] = progress
+                        self.download_status[shard.model_card.model_id] = progress
                         await self.event_sender.send(
                             NodeDownloadProgress(download_progress=progress)
                         )
@@ -339,7 +339,7 @@ class Worker:
                 initial_progress
             ),
         )
-        self.download_status[task.shard_metadata.model_meta.model_id] = status
+        self.download_status[task.shard_metadata.model_card.model_id] = status
         self.event_sender.send_nowait(NodeDownloadProgress(download_progress=status))
 
         last_progress_time = 0.0
@@ -356,7 +356,7 @@ class Worker:
                     node_id=self.node_id,
                     total_bytes=progress.total_bytes,
                 )
-                self.download_status[shard.model_meta.model_id] = status
+                self.download_status[shard.model_card.model_id] = status
                 await self.event_sender.send(
                     NodeDownloadProgress(download_progress=status)
                 )
@@ -376,7 +376,7 @@ class Worker:
                         progress
                     ),
                 )
-                self.download_status[shard.model_meta.model_id] = status
+                self.download_status[shard.model_card.model_id] = status
                 await self.event_sender.send(
                     NodeDownloadProgress(download_progress=status)
                 )
@@ -409,7 +409,7 @@ class Worker:
             conns = await check_reachable(
                 self.state.topology,
                 self.node_id,
-                self.state.node_profiles,
+                self.state.node_network,
             )
             for nid in conns:
                 for ip in conns[nid]:
@@ -478,7 +478,7 @@ class Worker:
                     else:
                         continue
 
-                    self.download_status[progress.shard.model_meta.model_id] = status
+                    self.download_status[progress.shard.model_card.model_id] = status
                     await self.event_sender.send(
                         NodeDownloadProgress(download_progress=status)
                     )

src/exo/worker/plan.py

@@ -2,8 +2,8 @@
 
 from collections.abc import Mapping, Sequence
 
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.common import NodeId
-from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import (
     ChatCompletion,
     ConnectToGroup,
@@ -114,7 +114,7 @@ def _model_needs_download(
     download_status: Mapping[ModelId, DownloadProgress],
 ) -> DownloadModel | None:
     for runner in runners.values():
-        model_id = runner.bound_instance.bound_shard.model_meta.model_id
+        model_id = runner.bound_instance.bound_shard.model_card.model_id
         if isinstance(runner.status, RunnerIdle) and (
             model_id not in download_status
             or not isinstance(
@@ -191,7 +191,7 @@ def _load_model(
             nid in global_download_status
             and any(
                 isinstance(dp, DownloadCompleted)
-                and dp.shard_metadata.model_meta.model_id == shard_assignments.model_id
+                and dp.shard_metadata.model_card.model_id == shard_assignments.model_id
                 for dp in global_download_status[nid]
             )
             for nid in shard_assignments.node_to_runner

src/exo/worker/runner/runner.py

@@ -4,6 +4,7 @@ from functools import cache
 
 import mlx.core as mx
 from mlx_lm.models.gpt_oss import Model as GptOssModel
+from mlx_lm.tokenizer_utils import TokenizerWrapper
 from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
     HarmonyEncodingName,
     Role,
@@ -50,6 +51,8 @@ from exo.shared.types.worker.runners import (
 from exo.utils.channels import MpReceiver, MpSender
 from exo.worker.engines.mlx.generator.generate import mlx_generate, warmup_inference
 from exo.worker.engines.mlx.utils_mlx import (
+    apply_chat_template,
+    detect_thinking_prompt_suffix,
     initialize_mlx,
     load_mlx_items,
     mlx_force_oom,
@@ -177,17 +180,28 @@ def main(
                     try:
                         _check_for_debug_prompts(task_params.messages[0].content)
 
+                        # Build prompt once - used for both generation and thinking detection
+                        prompt = apply_chat_template(tokenizer, task_params)
+
                         # Generate responses using the actual MLX generation
                         mlx_generator = mlx_generate(
                             model=model,
                             tokenizer=tokenizer,
                             task=task_params,
+                            prompt=prompt,
                         )
 
                         # GPT-OSS specific parsing to match other model formats.
                         if isinstance(model, GptOssModel):
                             mlx_generator = parse_gpt_oss(mlx_generator)
 
+                        # For other thinking models (GLM, etc.), check if we need to
+                        # prepend the thinking tag that was consumed by the chat template
+                        if detect_thinking_prompt_suffix(prompt, tokenizer):
+                            mlx_generator = parse_thinking_models(
+                                mlx_generator, tokenizer
+                            )
+
                         # TODO: Add tool call parser here
 
                         for response in mlx_generator:
@@ -199,7 +213,7 @@ def main(
                                                 command_id=command_id,
                                                 chunk=TokenChunk(
                                                     idx=response.token,
-                                                    model=shard_metadata.model_meta.model_id,
+                                                    model=shard_metadata.model_card.model_id,
                                                     text=response.text,
                                                     token_id=response.token,
                                                     finish_reason=response.finish_reason,
@@ -216,7 +230,7 @@ def main(
                                     command_id=command_id,
                                     chunk=TokenChunk(
                                         idx=0,
-                                        model=shard_metadata.model_meta.model_id,
+                                        model=shard_metadata.model_card.model_id,
                                         text="",
                                         token_id=0,
                                         finish_reason="error",
@@ -293,6 +307,28 @@ def parse_gpt_oss(
             break
 
 
+def parse_thinking_models(
+    responses: Generator[GenerationResponse],
+    tokenizer: TokenizerWrapper,
+) -> Generator[GenerationResponse]:
+    """
+    For models that inject thinking tags in the prompt (like GLM-4.7),
+    prepend the thinking tag to the output stream so the frontend
+    can properly parse thinking content.
+    """
+    first = True
+    for response in responses:
+        if first:
+            first = False
+            yield response.model_copy(
+                update={
+                    "text": tokenizer.think_start,
+                    "token": tokenizer.think_start_id,  # type: ignore
+                }
+            )
+        yield response
+
+
 EXO_RUNNER_MUST_FAIL = "EXO RUNNER MUST FAIL"
 EXO_RUNNER_MUST_OOM = "EXO RUNNER MUST OOM"
 EXO_RUNNER_MUST_TIMEOUT = "EXO RUNNER MUST TIMEOUT"

src/exo/worker/tests/constants.py

@@ -1,7 +1,7 @@
 from typing import Final
 
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.common import CommandId, NodeId
-from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import TaskId
 from exo.shared.types.worker.instances import InstanceId, RunnerId
 

src/exo/worker/tests/unittests/conftest.py

@@ -1,8 +1,8 @@
 from dataclasses import dataclass, field
 
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.common import NodeId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.tasks import BaseTask, TaskId
 from exo.shared.types.worker.instances import (
     BoundInstance,
@@ -32,9 +32,8 @@ def get_pipeline_shard_metadata(
     model_id: ModelId, device_rank: int, world_size: int = 1
 ) -> ShardMetadata:
     return PipelineShardMetadata(
-        model_meta=ModelMetadata(
+        model_card=ModelCard(
             model_id=model_id,
-            pretty_name=str(model_id),
             storage_size=Memory.from_mb(100000),
             n_layers=32,
             hidden_size=2048,

src/exo/worker/tests/unittests/test_mlx/conftest.py

@@ -11,9 +11,9 @@ import mlx.core as mx
 import mlx.nn as nn
 
 from exo.shared.constants import EXO_MODELS_DIR
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.api import ChatCompletionMessage
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.shared.types.worker.shards import PipelineShardMetadata, TensorShardMetadata
 from exo.worker.engines.mlx import Model
@@ -81,9 +81,8 @@ def run_gpt_oss_pipeline_device(
         start_layer, end_layer = layer_splits[rank]
 
         shard_meta = PipelineShardMetadata(
-            model_meta=ModelMetadata(
+            model_card=ModelCard(
                 model_id=ModelId(DEFAULT_GPT_OSS_MODEL_ID),
-                pretty_name="GPT-OSS 20B",
                 storage_size=Memory.from_gb(12),
                 n_layers=24,
                 hidden_size=2880,
@@ -151,9 +150,8 @@ def run_gpt_oss_tensor_parallel_device(
 
         # For tensor parallelism, all devices run all layers
         shard_meta = TensorShardMetadata(
-            model_meta=ModelMetadata(
+            model_card=ModelCard(
                 model_id=ModelId(DEFAULT_GPT_OSS_MODEL_ID),
-                pretty_name="GPT-OSS 20B",
                 storage_size=Memory.from_gb(12),
                 n_layers=24,
                 hidden_size=2880,

src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py

@@ -76,13 +76,13 @@ def get_test_models() -> list[tuple[str, ModelCard]]:
     """Get a representative sample of models to test."""
     # Pick one model from each family to test
     families: dict[str, tuple[str, ModelCard]] = {}
-    for short_id, card in MODEL_CARDS.items():
+    for _, card in MODEL_CARDS.items():
         # Extract family name (e.g., "llama-3.1" from "llama-3.1-8b")
-        parts = short_id.split("-")
+        parts = card.model_id.short().split("-")
         family = "-".join(parts[:2]) if len(parts) >= 2 else parts[0]
 
         if family not in families:
-            families[family] = (short_id, card)
+            families[family] = (card.model_id.short(), card)
 
     return list(families.values())
 

src/exo/worker/tests/unittests/test_plan/test_download_and_loading.py

@@ -1,7 +1,7 @@
 import exo.worker.plan as plan_mod
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.common import NodeId
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import LoadModel
 from exo.shared.types.worker.downloads import DownloadCompleted, DownloadProgress
 from exo.shared.types.worker.instances import BoundInstance

src/exo/worker/tests/unittests/test_runner/test_event_ordering.py

@@ -114,6 +114,10 @@ def patch_out_mlx(monkeypatch: pytest.MonkeyPatch):
     monkeypatch.setattr(mlx_runner, "load_mlx_items", make_nothin((1, 1)))
     monkeypatch.setattr(mlx_runner, "warmup_inference", make_nothin(1))
     monkeypatch.setattr(mlx_runner, "_check_for_debug_prompts", nothin)
+    # Mock apply_chat_template since we're using a fake tokenizer (integer 1).
+    # Returns a prompt without thinking tag so detect_thinking_prompt_suffix returns None.
+    monkeypatch.setattr(mlx_runner, "apply_chat_template", make_nothin("test prompt"))
+    monkeypatch.setattr(mlx_runner, "detect_thinking_prompt_suffix", make_nothin(False))
 
     def fake_generate(*_1: object, **_2: object):
         yield GenerationResponse(token=0, text="hi", finish_reason="stop")

tests/headless_runner.py

@@ -12,7 +12,7 @@ from loguru import logger
 from pydantic import BaseModel
 
 from exo.shared.logging import InterceptLogger, logger_setup
-from exo.shared.models.model_cards import MODEL_CARDS, ModelId
+from exo.shared.models.model_cards import ModelId
 from exo.shared.types.api import ChatCompletionMessage, ChatCompletionTaskParams
 from exo.shared.types.commands import CommandId
 from exo.shared.types.common import Host, NodeId
@@ -82,29 +82,29 @@ async def tb_detection():
     send, recv = channel[GatheredInfo]()
     ig = InfoGatherer(send)
     with anyio.move_on_after(1):
-        await ig._monitor_system_profiler()  # pyright: ignore[reportPrivateUsage]
+        await ig._monitor_system_profiler_thunderbolt_data()  # pyright: ignore[reportPrivateUsage]
     with recv:
         return recv.collect()
 
 
 async def assert_downloads():
     sd = exo_shard_downloader()
-    # await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-0.6b"].model_id))
+    # await sd.ensure_shard(ModelId("mlx-community/Qwen3-0.6B-8bit")))
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["llama-3.1-8b-bf16"].model_id)
+        await build_full_shard(ModelId("mlx-community/Llama-3.1-8b-bf16"))
     )
-    await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-30b"].model_id))
+    await sd.ensure_shard(await build_full_shard(ModelId("mlx-community/Qwen3-30b-A3B")))
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["gpt-oss-120b-MXFP4-Q8"].model_id)
+        await build_full_shard(ModelId("mlx-commmunity/gpt-oss-120b-MXFP4-Q8"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["gpt-oss-20b-4bit"].model_id)
+        await build_full_shard(ModelId("mlx-community/gpt-oss-20b-4bit"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["glm-4.7-8bit-gs32"].model_id)
+        await build_full_shard(ModelId("mlx-community/GLM-4.7-8bit-gs32"))
     )
     await sd.ensure_shard(
-        await build_full_shard(MODEL_CARDS["minimax-m2.1-8bit"].model_id)
+        await build_full_shard(ModelId("mlx-community/MiniMax-M2.1-8bit"))
     )
 
 
@@ -135,7 +135,7 @@ def ring_instance(test: Tests, iid: InstanceId, hn: str) -> Instance:
     else:
         raise ValueError(f"{hn} not in {test.devs}")
 
-    meta = MODEL_CARDS[test.model_id].metadata
+    card = MODEL_CARDS[test.model_id]
     instance = MlxRingInstance(
         instance_id=iid,
         ephemeral_port=52416,
@@ -145,15 +145,15 @@ def ring_instance(test: Tests, iid: InstanceId, hn: str) -> Instance:
             node_to_runner={NodeId(host[0]): RunnerId(host[0]) for host in test.devs},
             runner_to_shard={
                 RunnerId(test.devs[i][0]): PipelineShardMetadata(
-                    model_meta=meta,
+                    model_card=card,
                     device_rank=i,
                     world_size=world_size,
-                    start_layer=(meta.n_layers // world_size) * i,
+                    start_layer=(card.n_layers // world_size) * i,
                     end_layer=min(
-                        meta.n_layers, (meta.n_layers // world_size) * (i + 1)
+                        card.n_layers, (card.n_layers // world_size) * (i + 1)
                     ),
-                    n_layers=min(meta.n_layers, (meta.n_layers // world_size) * (i + 1))
-                    - (meta.n_layers // world_size) * i,
+                    n_layers=min(card.n_layers, (card.n_layers // world_size) * (i + 1))
+                    - (card.n_layers // world_size) * i,
                 )
                 for i in range(world_size)
             },
@@ -224,7 +224,7 @@ async def jaccl_backend(test: Tests):
 
 
 def jaccl_instance(test: Tests, iid: InstanceId):
-    meta = MODEL_CARDS[test.model_id].metadata
+    card = MODEL_CARDS[test.model_id]
     world_size = len(test.devs)
 
     return MlxJacclInstance(
@@ -239,12 +239,12 @@ def jaccl_instance(test: Tests, iid: InstanceId):
             node_to_runner={NodeId(host[0]): RunnerId(host[0]) for host in test.devs},
             runner_to_shard={
                 RunnerId(test.devs[i][0]): TensorShardMetadata(
-                    model_meta=meta,
+                    model_card=card,
                     device_rank=i,
                     world_size=world_size,
-                    start_layer=meta.n_layers,
-                    end_layer=meta.n_layers,
-                    n_layers=meta.n_layers,
+                    start_layer=card.n_layers,
+                    end_layer=card.n_layers,
+                    n_layers=card.n_layers,
                 )
                 for i in range(world_size)
             },