wuff

bench/exo_bench.py

@@ -496,9 +496,9 @@ def main() -> int:
                     and "tensor" in sharding.lower()
                 ):
                     model_card = MODEL_CARDS[short_id]
-                    if model_card.metadata.storage_size > Memory.from_gb(10):
+                    if model_card.storage_size > Memory.from_gb(10):
                         logger.info(
-                            f"Skipping tensor ring as this is too slow for model of size {model_card.metadata.storage_size} on {n_nodes=}"
+                            f"Skipping tensor ring as this is too slow for model of size {model_card.storage_size} on {n_nodes=}"
                         )
                         continue
                 for tg in tg_list:

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

@@ -71,36 +71,35 @@ export interface Instance {
 	};
 }
 
-// Split state interfaces
-interface RawNodeIdentity {
-	modelId: string;
-	chipId: string;
-	friendlyName: string;
-}
-
-interface RawNodeMemory {
-	ramTotal: { inBytes: number };
-	ramAvailable: { inBytes: number };
-	swapTotal: { inBytes: number };
-	swapAvailable: { inBytes: number };
-}
-
-interface RawNodeSystem {
-	gpuUsage?: number;
-	temp?: number;
-	sysPower?: number;
-	pcpuUsage?: number;
-	ecpuUsage?: number;
-	anePower?: number;
-}
-
-interface RawNetworkInterface {
-	name: string;
-	ipAddress: string;
+interface RawNodeProfile {
+	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 RawTopologyConnection {
@@ -116,6 +115,8 @@ interface RawTopology {
 	connections?: RawTopologyConnection[];
 }
 
+type RawNodeProfiles = Record<string, RawNodeProfile>;
+
 export interface DownloadProgress {
 	totalBytes: number;
 	downloadedBytes: number;
@@ -170,11 +171,7 @@ interface RawStateResponse {
 	>;
 	runners?: Record<string, unknown>;
 	downloads?: Record<string, unknown[]>;
-	// Split state fields
-	nodeIdentities?: Record<string, RawNodeIdentity>;
-	nodeMemories?: Record<string, RawNodeMemory>;
-	nodeSystems?: Record<string, RawNodeSystem>;
-	nodeNetworks?: Record<string, RawNetworkInterface[]>;
+	nodeProfiles?: RawNodeProfiles;
 }
 
 export interface MessageAttachment {
@@ -211,41 +208,66 @@ const STORAGE_KEY = "exo-conversations";
 
 function transformTopology(
 	raw: RawTopology,
-	identities?: Record<string, RawNodeIdentity>,
-	memories?: Record<string, RawNodeMemory>,
-	systems?: Record<string, RawNodeSystem>,
-	networks?: Record<string, RawNetworkInterface[]>,
+	profiles?: RawNodeProfiles,
 ): TopologyData {
 	const nodes: Record<string, NodeInfo> = {};
 	const edges: TopologyEdge[] = [];
 
 	for (const node of raw.nodes || []) {
-		// Get split state fields (may be undefined if events haven't arrived yet)
-		const identity = identities?.[node.nodeId];
-		const memory = memories?.[node.nodeId];
-		const system = systems?.[node.nodeId];
-		const network = networks?.[node.nodeId];
-
-		const ramTotal = memory?.ramTotal?.inBytes ?? 0;
-		const ramAvailable = memory?.ramAvailable?.inBytes ?? 0;
+		const mergedProfile = profiles?.[node.nodeId];
+		const profile = { ...(node.nodeProfile ?? {}), ...(mergedProfile ?? {}) };
+		const ramTotal = profile?.memory?.ramTotal?.inBytes ?? 0;
+		const ramAvailable = profile?.memory?.ramAvailable?.inBytes ?? 0;
 		const ramUsage = Math.max(ramTotal - ramAvailable, 0);
 
-		const networkInterfaces = (network ?? []).map((iface) => ({
-			name: iface.name,
-			addresses: [iface.ipAddress],
-		}));
+		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 ipToInterface: Record<string, string> = {};
 		for (const iface of networkInterfaces) {
-			for (const addr of iface.addresses) {
-				ipToInterface[addr] = iface.name;
+			for (const addr of iface.addresses || []) {
+				ipToInterface[addr] = iface.name ?? "";
 			}
 		}
 
 		nodes[node.nodeId] = {
 			system_info: {
-				model_id: identity?.modelId ?? "Unknown",
-				chip: identity?.chipId,
+				model_id: profile?.modelId ?? "Unknown",
+				chip: profile?.chipId,
 				memory: ramTotal,
 			},
 			network_interfaces: networkInterfaces,
@@ -256,15 +278,17 @@ function transformTopology(
 					ram_total: ramTotal,
 				},
 				temp:
-					system?.temp !== undefined
-						? { gpu_temp_avg: system.temp }
+					profile?.system?.temp !== undefined
+						? { gpu_temp_avg: profile.system.temp }
 						: undefined,
 				gpu_usage:
-					system?.gpuUsage !== undefined ? [0, system.gpuUsage] : undefined,
-				sys_power: system?.sysPower,
+					profile?.system?.gpuUsage !== undefined
+						? [0, profile.system.gpuUsage]
+						: undefined,
+				sys_power: profile?.system?.sysPower,
 			},
 			last_macmon_update: Date.now() / 1000,
-			friendly_name: identity?.friendlyName,
+			friendly_name: profile?.friendlyName,
 		};
 	}
 
@@ -844,13 +868,7 @@ class AppStore {
 			const data: RawStateResponse = await response.json();
 
 			if (data.topology) {
-				this.topologyData = transformTopology(
-					data.topology,
-					data.nodeIdentities,
-					data.nodeMemories,
-					data.nodeSystems,
-					data.nodeNetworks,
-				);
+				this.topologyData = transformTopology(data.topology, data.nodeProfiles);
 			}
 			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;

src/exo/master/api.py

@@ -19,8 +19,8 @@ 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 MODEL_CARDS, ModelCard, ModelId
+from exo.shared.models.model_meta import get_model_card
 from exo.shared.types.api import (
     BenchChatCompletionResponse,
     BenchChatCompletionTaskParams,
@@ -59,7 +59,6 @@ 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
@@ -87,12 +86,12 @@ def chunk_to_response(
     )
 
 
-async def resolve_model_meta(model_id: str) -> ModelMetadata:
+async def resolve_model_card(model_id: str) -> ModelCard:
     if model_id in MODEL_CARDS:
         model_card = MODEL_CARDS[model_id]
-        return model_card.metadata
+        return model_card
     else:
-        return await get_model_meta(model_id)
+        return await get_model_card(model_id)
 
 
 class API:
@@ -197,7 +196,7 @@ 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 +206,15 @@ 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 resolve_model_card(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 +231,7 @@ class API:
         return CreateInstanceResponse(
             message="Command received.",
             command_id=command.command_id,
-            model_meta=model_meta,
+            model_card=model_card,
         )
 
     async def get_placement(
@@ -242,12 +241,12 @@ 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,
@@ -278,7 +277,7 @@ 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 MODEL_CARDS.values() if card.model_id == model_id]
         if not cards:
             raise HTTPException(status_code=404, detail=f"Model {model_id} not found")
 
@@ -296,13 +295,12 @@ 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,
@@ -311,17 +309,17 @@ class API:
                         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())
@@ -332,17 +330,17 @@ 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]
@@ -351,7 +349,7 @@ 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)):
@@ -359,14 +357,14 @@ 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,
@@ -374,7 +372,7 @@ 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)
 
@@ -549,8 +547,8 @@ 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
@@ -576,8 +574,8 @@ 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
@@ -600,8 +598,9 @@ class API:
         """Calculate total available memory across all nodes in bytes."""
         total_available = Memory()
 
-        for memory in self.state.node_memories.values():
-            total_available += memory.ram_available
+        for node in self.state.topology.list_nodes():
+            if node.node_profile is not None:
+                total_available += node.node_profile.memory.ram_available
 
         return total_available
 
@@ -610,13 +609,13 @@ 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()
             ]

src/exo/master/placement.py

@@ -13,6 +13,7 @@ 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,
@@ -21,7 +22,6 @@ from exo.shared.types.commands import (
 )
 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.topology import NodeInfo
 from exo.shared.types.worker.instances import (
     Instance,
@@ -62,27 +62,27 @@ def place_instance(
         filter(lambda it: len(it) >= command.min_nodes, cycles + singleton_cycles)
     )
     cycles_with_sufficient_memory = filter_cycles_by_memory(
-        candidate_cycles, command.model_meta.storage_size
+        candidate_cycles, command.model_card.storage_size
     )
     if not cycles_with_sufficient_memory:
         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(
@@ -113,14 +113,13 @@ def place_instance(
                 node.node_profile.memory.ram_available
                 for node in cycle
                 if node.node_profile is not None
-                and node.node_profile.memory is not None
             ),
             start=Memory(),
         ),
     )
 
     shard_assignments = get_shard_assignments(
-        command.model_meta, selected_cycle, command.sharding
+        command.model_card, selected_cycle, command.sharding
     )
 
     cycle_digraph: Topology = topology.get_subgraph_from_nodes(selected_cycle)

src/exo/master/placement_utils.py

@@ -4,10 +4,10 @@ from typing import TypeGuard, cast
 from loguru import logger
 from pydantic import BaseModel
 
+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.topology import NodeInfo
 from exo.shared.types.worker.runners import RunnerId, ShardAssignments
@@ -25,10 +25,7 @@ class NodeWithProfile(BaseModel):
 
 
 def narrow_all_nodes(nodes: list[NodeInfo]) -> TypeGuard[list[NodeWithProfile]]:
-    return all(
-        node.node_profile is not None and node.node_profile.memory is not None
-        for node in nodes
-    )
+    return all(node.node_profile is not None for node in nodes)
 
 
 def filter_cycles_by_memory(
@@ -39,14 +36,8 @@ def filter_cycles_by_memory(
         if not narrow_all_nodes(cycle):
             continue
 
-        # narrow_all_nodes guarantees memory is not None
         total_mem = sum(
-            (
-                node.node_profile.memory.ram_available
-                for node in cycle
-                if node.node_profile.memory is not None
-            ),
-            start=Memory(),
+            (node.node_profile.memory.ram_available for node in cycle), start=Memory()
         )
         if total_mem >= required_memory:
             filtered_cycles.append(cast(list[NodeInfo], cycle))
@@ -90,25 +81,20 @@ def allocate_layers_proportionally(
 
 
 def get_shard_assignments_for_pipeline_parallel(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     selected_cycle: list[NodeWithProfile],
 ):
     if not selected_cycle:
         raise ValueError("Cannot create shard assignments for empty node cycle")
 
     cycle_memory = sum(
-        (
-            node.node_profile.memory.ram_available
-            for node in selected_cycle
-            if node.node_profile.memory is not None
-        ),
+        (node.node_profile.memory.ram_available for node in selected_cycle),
         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(selected_cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
@@ -118,16 +104,14 @@ def get_shard_assignments_for_pipeline_parallel(
         memory_fractions=[
             node.node_profile.memory.ram_available.in_bytes / cycle_memory.in_bytes
             for node in selected_cycle
-            if node.node_profile.memory is not None
         ],
     )
 
     # 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, node_layers) in enumerate(
         zip(selected_cycle, layer_allocations, strict=True)
     ):
-        assert node.node_profile.memory is not None
         required_memory = node_layers * memory_per_layer
         available_memory = node.node_profile.memory.ram_available.in_bytes
         if required_memory > available_memory:
@@ -144,7 +128,7 @@ 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,
@@ -157,7 +141,7 @@ 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,
     )
@@ -166,17 +150,17 @@ def get_shard_assignments_for_pipeline_parallel(
 
 
 def get_shard_assignments_for_tensor_parallel(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     selected_cycle: list[NodeWithProfile],
 ):
-    total_layers = model_meta.n_layers
+    total_layers = model_card.n_layers
     world_size = len(selected_cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
 
     for i, node in enumerate(selected_cycle):
         shard = TensorShardMetadata(
-            model_meta=model_meta,
+            model_card=model_card,
             device_rank=i,
             world_size=world_size,
             start_layer=0,
@@ -190,7 +174,7 @@ def get_shard_assignments_for_tensor_parallel(
         node_to_runner[node.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,
     )
@@ -199,7 +183,7 @@ def get_shard_assignments_for_tensor_parallel(
 
 
 def get_shard_assignments(
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     selected_cycle: list[NodeInfo],
     sharding: Sharding,
 ) -> ShardAssignments:
@@ -208,12 +192,12 @@ def get_shard_assignments(
     match sharding:
         case Sharding.Pipeline:
             return get_shard_assignments_for_pipeline_parallel(
-                model_meta=model_meta,
+                model_card=model_card,
                 selected_cycle=selected_cycle,
             )
         case Sharding.Tensor:
             return get_shard_assignments_for_tensor_parallel(
-                model_meta=model_meta,
+                model_card=model_card,
                 selected_cycle=selected_cycle,
             )
 

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,
@@ -19,13 +20,15 @@ from exo.shared.types.events import (
     ForwarderEvent,
     IndexedEvent,
     InstanceCreated,
-    NodeIdentityMeasured,
-    NodeMemoryMeasured,
+    NodePerformanceMeasured,
     TaskCreated,
 )
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
-from exo.shared.types.profiling import MemoryPerformanceProfile
+from exo.shared.types.profiling import (
+    MemoryPerformanceProfile,
+    NodePerformanceProfile,
+    SystemPerformanceProfile,
+)
 from exo.shared.types.tasks import ChatCompletion as ChatCompletionTask
 from exo.shared.types.tasks import TaskStatus
 from exo.shared.types.worker.instances import (
@@ -72,39 +75,29 @@ async def test_master():
         tg.start_soon(master.run)
 
         sender_node_id = NodeId(f"{keypair.to_peer_id().to_base58()}_sender")
-        # inject NodeIdentityMeasured and NodeMemoryMeasured events
-        logger.info("inject NodeIdentityMeasured event")
+        # inject a NodePerformanceProfile event
+        logger.info("inject a NodePerformanceProfile event")
         await local_event_sender.send(
             ForwarderEvent(
                 origin_idx=0,
                 origin=sender_node_id,
                 session=session_id,
                 event=(
-                    NodeIdentityMeasured(
+                    NodePerformanceMeasured(
                         when=str(datetime.now(tz=timezone.utc)),
                         node_id=node_id,
-                        model_id="maccy",
-                        chip_id="arm",
-                        friendly_name="test",
-                    )
-                ),
-            )
-        )
-        logger.info("inject NodeMemoryMeasured event")
-        await local_event_sender.send(
-            ForwarderEvent(
-                origin_idx=1,
-                origin=sender_node_id,
-                session=session_id,
-                event=(
-                    NodeMemoryMeasured(
-                        when=str(datetime.now(tz=timezone.utc)),
-                        node_id=node_id,
-                        memory=MemoryPerformanceProfile(
-                            ram_total=Memory.from_bytes(678948 * 1024),
-                            ram_available=Memory.from_bytes(678948 * 1024),
-                            swap_total=Memory.from_bytes(0),
-                            swap_available=Memory.from_bytes(0),
+                        node_profile=NodePerformanceProfile(
+                            model_id="maccy",
+                            chip_id="arm",
+                            friendly_name="test",
+                            memory=MemoryPerformanceProfile(
+                                ram_total=Memory.from_bytes(678948 * 1024),
+                                ram_available=Memory.from_bytes(678948 * 1024),
+                                swap_total=Memory.from_bytes(0),
+                                swap_available=Memory.from_bytes(0),
+                            ),
+                            network_interfaces=[],
+                            system=SystemPerformanceProfile(),
                         ),
                     )
                 ),
@@ -115,7 +108,7 @@ 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_identities) == 0:
+        while len(master.state.node_profiles) == 0:
             await anyio.sleep(0.001)
 
         logger.info("inject a CreateInstance Command")
@@ -125,9 +118,8 @@ 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,
@@ -162,19 +154,17 @@ async def test_master():
                 ),
             )
         )
-        while len(_get_events()) < 4:
+        while len(_get_events()) < 3:
             await anyio.sleep(0.01)
 
         events = _get_events()
-        assert len(events) == 4
+        assert len(events) == 3
         assert events[0].idx == 0
         assert events[1].idx == 1
         assert events[2].idx == 2
-        assert events[3].idx == 3
-        assert isinstance(events[0].event, NodeIdentityMeasured)
-        assert isinstance(events[1].event, NodeMemoryMeasured)
-        assert isinstance(events[2].event, InstanceCreated)
-        created_instance = events[2].event.instance
+        assert isinstance(events[0].event, NodePerformanceMeasured)
+        assert isinstance(events[1].event, InstanceCreated)
+        created_instance = events[1].event.instance
         assert isinstance(created_instance, MlxRingInstance)
         runner_id = list(created_instance.shard_assignments.runner_to_shard.keys())[0]
         # Validate the shard assignments
@@ -185,9 +175,8 @@ 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,
@@ -206,10 +195,10 @@ async def test_master():
         assert len(created_instance.hosts_by_node[node_id]) == 1
         assert created_instance.hosts_by_node[node_id][0].ip == "0.0.0.0"
         assert created_instance.ephemeral_port > 0
-        assert isinstance(events[3].event, TaskCreated)
-        assert events[3].event.task.task_status == TaskStatus.Pending
-        assert isinstance(events[3].event.task, ChatCompletionTask)
-        assert events[3].event.task.task_params == ChatCompletionTaskParams(
+        assert isinstance(events[2].event, TaskCreated)
+        assert events[2].event.task.task_status == TaskStatus.Pending
+        assert isinstance(events[2].event.task, ChatCompletionTask)
+        assert events[2].event.task.task_params == ChatCompletionTaskParams(
             model="llama-3.2-1b",
             messages=[
                 ChatCompletionMessage(role="user", content="Hello, how are you?")

src/exo/master/tests/test_placement.py

@@ -7,12 +7,12 @@ from exo.master.placement import (
     get_transition_events,
     place_instance,
 )
+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.profiling import NetworkInterfaceInfo, NodePerformanceProfile
 from exo.shared.types.topology import Connection, NodeInfo
 from exo.shared.types.worker.instances import (
@@ -44,21 +44,20 @@ 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,
@@ -78,17 +77,17 @@ def test_get_instance_placements_create_instance(
     total_layers: int,
     expected_layers: tuple[int, int, int],
     topology: Topology,
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     create_node: Callable[[int, NodeId | None], NodeInfo],
     create_connection: Callable[[NodeId, NodeId], Connection],
 ):
     # 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
 
-    cic = place_instance_command(model_meta)
+    cic = place_instance_command(model_card)
     node_id_a = NodeId()
     node_id_b = NodeId()
     node_id_c = NodeId()
@@ -110,7 +109,7 @@ def test_get_instance_placements_create_instance(
     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]
@@ -137,10 +136,9 @@ def test_get_instance_placements_one_node_exact_fit(
     node_id = NodeId()
     topology.add_node(create_node(1000 * 1024, node_id))
     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,
@@ -164,10 +162,9 @@ def test_get_instance_placements_one_node_fits_with_extra_memory(
     node_id = NodeId()
     topology.add_node(create_node(1001 * 1024, node_id))
     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,
@@ -191,10 +188,9 @@ def test_get_instance_placements_one_node_not_fit(
     node_id = NodeId()
     topology.add_node(create_node(1000 * 1024, node_id))
     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,
@@ -249,7 +245,7 @@ def test_get_transition_events_delete_instance(instance: Instance):
 
 def test_placement_selects_cycle_with_most_memory(
     topology: Topology,
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     create_node: Callable[[int, NodeId | None], NodeInfo],
     create_connection: Callable[[NodeId, NodeId], Connection],
 ):
@@ -258,8 +254,8 @@ def test_placement_selects_cycle_with_most_memory(
     # The algorithm should select the cycle with the most available memory.
 
     # Model requires more than any single node but fits within a 3-node cycle
-    model_meta.storage_size.in_bytes = 1500
-    model_meta.n_layers = 12
+    model_card.storage_size.in_bytes = 1500
+    model_card.n_layers = 12
 
     # Create node ids
     node_id_a = NodeId()
@@ -295,7 +291,7 @@ def test_placement_selects_cycle_with_most_memory(
     topology.add_connection(create_connection(node_id_d, node_id_f))
 
     cic = place_instance_command(
-        model_meta=model_meta,
+        model_card=model_card,
     )
 
     # Act
@@ -316,12 +312,12 @@ def test_placement_selects_cycle_with_most_memory(
 
 def test_tensor_rdma_backend_connectivity_matrix(
     topology: Topology,
-    model_meta: ModelMetadata,
+    model_card: ModelCard,
     create_node: Callable[[int, NodeId | None], NodeInfo],
     create_connection: Callable[[NodeId, NodeId], Connection],
 ):
-    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_id_a = NodeId()
     node_id_b = NodeId()
@@ -425,7 +421,7 @@ 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,
     )
 

src/exo/master/tests/test_placement_utils.py

@@ -10,10 +10,10 @@ from exo.master.placement_utils import (
     get_shard_assignments,
     get_smallest_cycles,
 )
+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 NetworkInterfaceInfo, NodePerformanceProfile
 from exo.shared.types.topology import Connection, NodeInfo
 from exo.shared.types.worker.shards import Sharding
@@ -197,9 +197,8 @@ def test_get_shard_assignments(
     topology.add_connection(create_connection(node_c_id, node_a_id))
     topology.add_connection(create_connection(node_b_id, node_a_id))
 
-    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,
@@ -210,7 +209,7 @@ def test_get_shard_assignments(
 
     # act
     shard_assignments = get_shard_assignments(
-        model_meta, selected_cycle, Sharding.Pipeline
+        model_card, selected_cycle, Sharding.Pipeline
     )
 
     # assert
@@ -481,9 +480,8 @@ def test_get_shard_assignments_insufficient_memory_raises(
     topology.add_connection(create_connection(node_c_id, node_a_id))
     topology.add_connection(create_connection(node_b_id, node_a_id))
 
-    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,
@@ -493,4 +491,4 @@ 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)
+        get_shard_assignments(model_card, selected_cycle, Sharding.Pipeline)

src/exo/shared/apply.py

@@ -13,10 +13,8 @@ from exo.shared.types.events import (
     InstanceDeleted,
     NodeCreated,
     NodeDownloadProgress,
-    NodeIdentityMeasured,
     NodeMemoryMeasured,
-    NodeNetworkMeasured,
-    NodeSystemMeasured,
+    NodePerformanceMeasured,
     NodeTimedOut,
     RunnerDeleted,
     RunnerStatusUpdated,
@@ -29,13 +27,7 @@ from exo.shared.types.events import (
     TopologyEdgeCreated,
     TopologyEdgeDeleted,
 )
-from exo.shared.types.profiling import (
-    MemoryPerformanceProfile,
-    NetworkInterfaceInfo,
-    NodeIdentity,
-    NodePerformanceProfile,
-    SystemPerformanceProfile,
-)
+from exo.shared.types.profiling import NodePerformanceProfile, SystemPerformanceProfile
 from exo.shared.types.state import State
 from exo.shared.types.tasks import Task, TaskId, TaskStatus
 from exo.shared.types.topology import NodeInfo
@@ -59,12 +51,8 @@ def event_apply(event: Event, state: State) -> State:
             return apply_topology_node_created(event, state)
         case NodeTimedOut():
             return apply_node_timed_out(event, state)
-        case NodeIdentityMeasured():
-            return apply_node_identity_measured(event, state)
-        case NodeSystemMeasured():
-            return apply_node_system_measured(event, state)
-        case NodeNetworkMeasured():
-            return apply_node_network_measured(event, state)
+        case NodePerformanceMeasured():
+            return apply_node_performance_measured(event, state)
         case NodeDownloadProgress():
             return apply_node_download_progress(event, state)
         case NodeMemoryMeasured():
@@ -202,19 +190,8 @@ def apply_runner_deleted(event: RunnerDeleted, state: State) -> State:
 def apply_node_timed_out(event: NodeTimedOut, state: State) -> State:
     topology = copy.copy(state.topology)
     state.topology.remove_node(event.node_id)
-    node_identities = {
-        key: value
-        for key, value in state.node_identities.items()
-        if key != event.node_id
-    }
-    node_memories = {
-        key: value for key, value in state.node_memories.items() if key != event.node_id
-    }
-    node_systems = {
-        key: value for key, value in state.node_systems.items() if key != event.node_id
-    }
-    node_networks = {
-        key: value for key, value in state.node_networks.items() if key != event.node_id
+    node_profiles = {
+        key: value for key, value in state.node_profiles.items() if key != event.node_id
     }
     last_seen = {
         key: value for key, value in state.last_seen.items() if key != event.node_id
@@ -222,120 +199,32 @@ def apply_node_timed_out(event: NodeTimedOut, state: State) -> State:
     return state.model_copy(
         update={
             "topology": topology,
-            "node_identities": node_identities,
-            "node_memories": node_memories,
-            "node_systems": node_systems,
-            "node_networks": node_networks,
+            "node_profiles": node_profiles,
             "last_seen": last_seen,
         }
     )
 
 
-def _reconstruct_profile(
-    node_id: NodeId,
-    state: State,
-    *,
-    identity: NodeIdentity | None = None,
-    memory: MemoryPerformanceProfile | None = None,
-    system: SystemPerformanceProfile | None = None,
-    network_interfaces: list[NetworkInterfaceInfo] | None = None,
-) -> NodePerformanceProfile:
-    """Reconstruct a NodePerformanceProfile from split state storage.
-
-    Uses provided overrides, falling back to state values.
-    """
-    ident = identity or state.node_identities.get(node_id)
-    mem = memory or state.node_memories.get(node_id)
-    sys = system or state.node_systems.get(node_id)
-    nets = (
-        network_interfaces
-        if network_interfaces is not None
-        else state.node_networks.get(node_id, [])
-    )
-
-    return NodePerformanceProfile(
-        model_id=ident.model_id if ident else None,
-        chip_id=ident.chip_id if ident else None,
-        friendly_name=ident.friendly_name if ident else None,
-        memory=mem,
-        network_interfaces=nets,
-        system=sys,
-    )
-
-
-def apply_node_identity_measured(event: NodeIdentityMeasured, state: State) -> State:
-    topology = copy.copy(state.topology)
-
-    identity = NodeIdentity(
-        model_id=event.model_id,
-        chip_id=event.chip_id,
-        friendly_name=event.friendly_name,
-    )
-    new_identities: Mapping[NodeId, NodeIdentity] = {
-        **state.node_identities,
-        event.node_id: identity,
+def apply_node_performance_measured(
+    event: NodePerformanceMeasured, state: State
+) -> State:
+    new_profiles: Mapping[NodeId, NodePerformanceProfile] = {
+        **state.node_profiles,
+        event.node_id: event.node_profile,
     }
     last_seen: Mapping[NodeId, datetime] = {
         **state.last_seen,
         event.node_id: datetime.fromisoformat(event.when),
     }
-    if not topology.contains_node(event.node_id):
-        topology.add_node(NodeInfo(node_id=event.node_id))
-    reconstructed = _reconstruct_profile(event.node_id, state, identity=identity)
-    topology.update_node_profile(event.node_id, reconstructed)
-    return state.model_copy(
-        update={
-            "node_identities": new_identities,
-            "topology": topology,
-            "last_seen": last_seen,
-        }
-    )
-
-
-def apply_node_system_measured(event: NodeSystemMeasured, state: State) -> State:
+    state = state.model_copy(update={"node_profiles": new_profiles})
     topology = copy.copy(state.topology)
-
-    new_systems: Mapping[NodeId, SystemPerformanceProfile] = {
-        **state.node_systems,
-        event.node_id: event.system,
-    }
-    last_seen: Mapping[NodeId, datetime] = {
-        **state.last_seen,
-        event.node_id: datetime.fromisoformat(event.when),
-    }
+    # TODO: NodeCreated
     if not topology.contains_node(event.node_id):
         topology.add_node(NodeInfo(node_id=event.node_id))
-    reconstructed = _reconstruct_profile(event.node_id, state, system=event.system)
-    topology.update_node_profile(event.node_id, reconstructed)
+    topology.update_node_profile(event.node_id, event.node_profile)
     return state.model_copy(
         update={
-            "node_systems": new_systems,
-            "topology": topology,
-            "last_seen": last_seen,
-        }
-    )
-
-
-def apply_node_network_measured(event: NodeNetworkMeasured, state: State) -> State:
-    topology = copy.copy(state.topology)
-
-    new_networks: Mapping[NodeId, list[NetworkInterfaceInfo]] = {
-        **state.node_networks,
-        event.node_id: event.network_interfaces,
-    }
-    last_seen: Mapping[NodeId, datetime] = {
-        **state.last_seen,
-        event.node_id: datetime.fromisoformat(event.when),
-    }
-    if not topology.contains_node(event.node_id):
-        topology.add_node(NodeInfo(node_id=event.node_id))
-    reconstructed = _reconstruct_profile(
-        event.node_id, state, network_interfaces=event.network_interfaces
-    )
-    topology.update_node_profile(event.node_id, reconstructed)
-    return state.model_copy(
-        update={
-            "node_networks": new_networks,
+            "node_profiles": new_profiles,
             "topology": topology,
             "last_seen": last_seen,
         }
@@ -343,26 +232,57 @@ def apply_node_network_measured(event: NodeNetworkMeasured, state: State) -> Sta
 
 
 def apply_node_memory_measured(event: NodeMemoryMeasured, state: State) -> State:
+    existing = state.node_profiles.get(event.node_id)
     topology = copy.copy(state.topology)
 
-    new_memories: Mapping[NodeId, MemoryPerformanceProfile] = {
-        **state.node_memories,
-        event.node_id: event.memory,
-    }
-    last_seen: Mapping[NodeId, datetime] = {
-        **state.last_seen,
-        event.node_id: datetime.fromisoformat(event.when),
+    if existing is None:
+        created = NodePerformanceProfile(
+            model_id="unknown",
+            chip_id="unknown",
+            friendly_name="Unknown",
+            memory=event.memory,
+            network_interfaces=[],
+            system=SystemPerformanceProfile(
+                # TODO: flops_fp16=0.0,
+                gpu_usage=0.0,
+                temp=0.0,
+                sys_power=0.0,
+                pcpu_usage=0.0,
+                ecpu_usage=0.0,
+                ane_power=0.0,
+            ),
+        )
+        created_profiles: Mapping[NodeId, NodePerformanceProfile] = {
+            **state.node_profiles,
+            event.node_id: created,
+        }
+        last_seen: Mapping[NodeId, datetime] = {
+            **state.last_seen,
+            event.node_id: datetime.fromisoformat(event.when),
+        }
+        if not topology.contains_node(event.node_id):
+            topology.add_node(NodeInfo(node_id=event.node_id))
+            # TODO: NodeCreated
+        topology.update_node_profile(event.node_id, created)
+        return state.model_copy(
+            update={
+                "node_profiles": created_profiles,
+                "topology": topology,
+                "last_seen": last_seen,
+            }
+        )
+
+    updated = existing.model_copy(update={"memory": event.memory})
+    updated_profiles: Mapping[NodeId, NodePerformanceProfile] = {
+        **state.node_profiles,
+        event.node_id: updated,
     }
+    # TODO: NodeCreated
     if not topology.contains_node(event.node_id):
         topology.add_node(NodeInfo(node_id=event.node_id))
-    reconstructed = _reconstruct_profile(event.node_id, state, memory=event.memory)
-    topology.update_node_profile(event.node_id, reconstructed)
+    topology.update_node_profile(event.node_id, updated)
     return state.model_copy(
-        update={
-            "node_memories": new_memories,
-            "topology": topology,
-            "last_seen": last_seen,
-        }
+        update={"node_profiles": updated_profiles, "topology": topology}
     )
 
 

src/exo/shared/models/model_cards.py

@@ -1,552 +1,281 @@
+from pydantic import PositiveInt
+
+from exo.shared.types.common import Id
 from exo.shared.types.memory import Memory
-from exo.shared.types.models import ModelId, ModelMetadata
 from exo.utils.pydantic_ext import CamelCaseModel
 
 
+class ModelId(Id):
+    def normalize(self) -> str:
+        return self.replace("/", "--")
+
+    def short(self) -> str:
+        return self.split("/")[-1]
+
+
 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
 
 
 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,
-        ),
+        storage_size=Memory.from_gb(378),
+        n_layers=61,
+        hidden_size=7168,
+        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,
-        ),
+        storage_size=Memory.from_gb(713),
+        n_layers=61,
+        hidden_size=7168,
+        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,
-        ),
+        storage_size=Memory.from_gb(578),
+        n_layers=61,
+        hidden_size=7168,
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        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,
-        ),
+        storage_size=Memory.from_bytes(100086644736),
+        n_layers=61,
+        hidden_size=3072,
+        supports_tensor=True,
     ),
 }

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 MODEL_CARDS, 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,
@@ -92,18 +91,18 @@ async def get_safetensors_size(model_id: str) -> Memory:
     return Memory.from_bytes(info.safetensors.total)
 
 
-_model_meta_cache: dict[str, ModelMetadata] = {}
+_model_card_cache: dict[str, ModelCard] = {}
 
 
-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_card(model_id: str) -> ModelCard:
+    if model_id in _model_card_cache:
+        return _model_card_cache[model_id]
+    model_card = await _get_model_card(model_id)
+    _model_card_cache[model_id] = model_card
+    return model_card
 
 
-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."""
     config_data = await get_config_data(model_id)
     num_layers = config_data.layer_count
@@ -113,14 +112,11 @@ async def _get_model_meta(model_id: str) -> ModelMetadata:
         None,
     )
 
-    return ModelMetadata(
+    return 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=model_card.supports_tensor if model_card is not None else False,
     )

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/events.py

@@ -2,14 +2,10 @@ from datetime import datetime
 
 from pydantic import Field
 
-from exo.shared.topology import Connection
+from exo.shared.topology import Connection, NodePerformanceProfile
 from exo.shared.types.chunks import GenerationChunk
 from exo.shared.types.common import CommandId, Id, NodeId, SessionId
-from exo.shared.types.profiling import (
-    MemoryPerformanceProfile,
-    NetworkInterfaceInfo,
-    SystemPerformanceProfile,
-)
+from exo.shared.types.profiling import MemoryPerformanceProfile
 from exo.shared.types.tasks import Task, TaskId, TaskStatus
 from exo.shared.types.worker.downloads import DownloadProgress
 from exo.shared.types.worker.instances import Instance, InstanceId
@@ -89,35 +85,13 @@ class NodeTimedOut(BaseEvent):
     node_id: NodeId
 
 
-class NodeIdentityMeasured(BaseEvent):
-    """Static identity info - emitted once at startup."""
-
+class NodePerformanceMeasured(BaseEvent):
     node_id: NodeId
     when: str  # this is a manually cast datetime overrode by the master when the event is indexed, rather than the local time on the device
-    model_id: str
-    chip_id: str
-    friendly_name: str
-
-
-class NodeSystemMeasured(BaseEvent):
-    """Dynamic system metrics (GPU, temp, power) - emitted at 1s intervals."""
-
-    node_id: NodeId
-    when: str  # this is a manually cast datetime overrode by the master when the event is indexed, rather than the local time on the device
-    system: SystemPerformanceProfile
-
-
-class NodeNetworkMeasured(BaseEvent):
-    """Semi-static network interface info - emitted at 30s intervals."""
-
-    node_id: NodeId
-    when: str  # this is a manually cast datetime overrode by the master when the event is indexed, rather than the local time on the device
-    network_interfaces: list[NetworkInterfaceInfo]
+    node_profile: NodePerformanceProfile
 
 
 class NodeMemoryMeasured(BaseEvent):
-    """Dynamic memory metrics - emitted at 0.5s intervals."""
-
     node_id: NodeId
     when: str  # this is a manually cast datetime overrode by the master when the event is indexed, rather than the local time on the device
     memory: MemoryPerformanceProfile
@@ -153,9 +127,7 @@ Event = (
     | RunnerDeleted
     | NodeCreated
     | NodeTimedOut
-    | NodeIdentityMeasured
-    | NodeSystemMeasured
-    | NodeNetworkMeasured
+    | NodePerformanceMeasured
     | NodeMemoryMeasured
     | NodeDownloadProgress
     | ChunkGenerated

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

@@ -52,21 +52,13 @@ class NetworkInterfaceInfo(CamelCaseModel):
     ip_address: str
 
 
-class NodeIdentity(CamelCaseModel):
-    """Static identity info for a node."""
-
+class NodePerformanceProfile(CamelCaseModel):
     model_id: str
     chip_id: str
     friendly_name: str
-
-
-class NodePerformanceProfile(CamelCaseModel):
-    model_id: str | None = None
-    chip_id: str | None = None
-    friendly_name: str | None = None
-    memory: MemoryPerformanceProfile | None = None
+    memory: MemoryPerformanceProfile
     network_interfaces: list[NetworkInterfaceInfo] = []
-    system: SystemPerformanceProfile | None = None
+    system: SystemPerformanceProfile
 
 
 class ConnectionProfile(CamelCaseModel):

src/exo/shared/types/state.py

@@ -7,12 +7,7 @@ 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 (
-    MemoryPerformanceProfile,
-    NetworkInterfaceInfo,
-    NodeIdentity,
-    SystemPerformanceProfile,
-)
+from exo.shared.types.profiling import NodePerformanceProfile
 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
@@ -40,10 +35,7 @@ class State(CamelCaseModel):
     runners: Mapping[RunnerId, RunnerStatus] = {}
     downloads: Mapping[NodeId, Sequence[DownloadProgress]] = {}
     tasks: Mapping[TaskId, Task] = {}
-    node_identities: Mapping[NodeId, NodeIdentity] = {}
-    node_memories: Mapping[NodeId, MemoryPerformanceProfile] = {}
-    node_systems: Mapping[NodeId, SystemPerformanceProfile] = {}
-    node_networks: Mapping[NodeId, list[NetworkInterfaceInfo]] = {}
+    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)

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/worker/download/download_utils.py

@@ -459,10 +459,10 @@ 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 ["*"]
 
@@ -531,18 +531,18 @@ 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:
@@ -551,13 +551,13 @@ 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(
@@ -591,7 +591,7 @@ 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),
@@ -608,7 +608,7 @@ 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,
@@ -618,7 +618,7 @@ 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),
@@ -635,7 +635,7 @@ async def download_shard(
     async def download_with_semaphore(file: FileListEntry):
         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,
@@ -649,7 +649,7 @@ 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
     )
     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

@@ -3,7 +3,7 @@ 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_meta import get_model_card
 from exo.shared.types.worker.shards import (
     PipelineShardMetadata,
     ShardMetadata,
@@ -19,21 +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 get_model_card(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,
@@ -90,11 +90,11 @@ 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(

src/exo/worker/download/shard_downloader.py

@@ -4,8 +4,8 @@ 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,
@@ -83,9 +83,8 @@ 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/auto_parallel.py

@@ -46,11 +46,9 @@ class CustomMlxLayer(nn.Module):
 
     def __init__(self, original_layer: _LayerCallable):
         super().__init__()
+        # Set twice to avoid __setattr__ recursion
         object.__setattr__(self, "_original_layer", original_layer)
-
-    @property
-    def original_layer(self) -> _LayerCallable:
-        return cast(_LayerCallable, object.__getattribute__(self, "_original_layer"))
+        self.original_layer: _LayerCallable = original_layer
 
     # Calls __getattr__ for any attributes not found on nn.Module (e.g. use_sliding)
     if not TYPE_CHECKING:
@@ -60,7 +58,7 @@ class CustomMlxLayer(nn.Module):
                 return super().__getattr__(name)
             except AttributeError:
                 original_layer = object.__getattribute__(self, "_original_layer")
-                return getattr(original_layer, name)
+                return object.__getattribute__(original_layer, name)
 
 
 class PipelineFirstLayer(CustomMlxLayer):

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

@@ -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)
@@ -235,7 +235,7 @@ 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()
@@ -263,7 +263,7 @@ 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)
@@ -322,7 +322,7 @@ 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:

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 (
@@ -16,22 +17,15 @@ from exo.shared.types.events import (
     ForwarderEvent,
     IndexedEvent,
     NodeDownloadProgress,
-    NodeIdentityMeasured,
     NodeMemoryMeasured,
-    NodeNetworkMeasured,
-    NodeSystemMeasured,
+    NodePerformanceMeasured,
     TaskCreated,
     TaskStatusUpdated,
     TopologyEdgeCreated,
     TopologyEdgeDeleted,
 )
-from exo.shared.types.models import ModelId
 from exo.shared.types.multiaddr import Multiaddr
-from exo.shared.types.profiling import (
-    MemoryPerformanceProfile,
-    NetworkInterfaceInfo,
-    SystemPerformanceProfile,
-)
+from exo.shared.types.profiling import MemoryPerformanceProfile, NodePerformanceProfile
 from exo.shared.types.state import State
 from exo.shared.types.tasks import (
     CreateRunner,
@@ -57,13 +51,7 @@ from exo.worker.download.download_utils import (
 from exo.worker.download.shard_downloader import RepoDownloadProgress, ShardDownloader
 from exo.worker.plan import plan
 from exo.worker.runner.runner_supervisor import RunnerSupervisor
-from exo.worker.utils import (
-    IdentityMetrics,
-    start_polling_identity_metrics,
-    start_polling_memory_metrics,
-    start_polling_network_metrics,
-    start_polling_system_metrics,
-)
+from exo.worker.utils import start_polling_memory_metrics, start_polling_node_metrics
 from exo.worker.utils.net_profile import check_reachable
 
 
@@ -110,51 +98,37 @@ class Worker:
     async def run(self):
         logger.info("Starting Worker")
 
-        async def identity_callback(identity: IdentityMetrics) -> None:
+        # TODO: CLEANUP HEADER
+        async def resource_monitor_callback(
+            node_performance_profile: NodePerformanceProfile,
+        ) -> None:
             await self.event_sender.send(
-                NodeIdentityMeasured(
+                NodePerformanceMeasured(
                     node_id=self.node_id,
-                    model_id=identity.model_id,
-                    chip_id=identity.chip_id,
-                    friendly_name=identity.friendly_name,
+                    node_profile=node_performance_profile,
                     when=str(datetime.now(tz=timezone.utc)),
                 ),
             )
 
-        async def system_callback(system: SystemPerformanceProfile) -> None:
-            await self.event_sender.send(
-                NodeSystemMeasured(
-                    node_id=self.node_id,
-                    system=system,
-                    when=str(datetime.now(tz=timezone.utc)),
-                ),
-            )
-
-        async def network_callback(interfaces: list[NetworkInterfaceInfo]) -> None:
-            await self.event_sender.send(
-                NodeNetworkMeasured(
-                    node_id=self.node_id,
-                    network_interfaces=interfaces,
-                    when=str(datetime.now(tz=timezone.utc)),
-                ),
-            )
-
-        async def memory_callback(memory: MemoryPerformanceProfile) -> None:
+        async def memory_monitor_callback(
+            memory_profile: MemoryPerformanceProfile,
+        ) -> None:
             await self.event_sender.send(
                 NodeMemoryMeasured(
                     node_id=self.node_id,
-                    memory=memory,
+                    memory=memory_profile,
                     when=str(datetime.now(tz=timezone.utc)),
                 )
             )
 
+        # END CLEANUP
+
         async with create_task_group() as tg:
             self._tg = tg
             tg.start_soon(self.plan_step)
-            tg.start_soon(start_polling_identity_metrics, identity_callback)
-            tg.start_soon(start_polling_system_metrics, system_callback)
-            tg.start_soon(start_polling_network_metrics, network_callback)
-            tg.start_soon(start_polling_memory_metrics, memory_callback)
+            tg.start_soon(start_polling_node_metrics, resource_monitor_callback)
+
+            tg.start_soon(start_polling_memory_metrics, memory_monitor_callback)
             tg.start_soon(self._emit_existing_download_progress)
             tg.start_soon(self._connection_message_event_writer)
             tg.start_soon(self._resend_out_for_delivery)
@@ -228,11 +202,11 @@ 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)
                         )
@@ -247,7 +221,7 @@ 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)
                         )
@@ -379,7 +353,7 @@ 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
@@ -397,7 +371,7 @@ 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
                 # Footgun!
                 self.event_sender.send_nowait(
                     NodeDownloadProgress(download_progress=status)
@@ -418,7 +392,7 @@ class Worker:
                         progress
                     ),
                 )
-                self.download_status[shard.model_meta.model_id] = status
+                self.download_status[shard.model_card.model_id] = status
                 self.event_sender.send_nowait(
                     NodeDownloadProgress(download_progress=status)
                 )
@@ -509,7 +483,7 @@ 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

@@ -199,7 +199,7 @@ 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 +216,7 @@ 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",

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

@@ -1,202 +0,0 @@
-# type: ignore
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-import mlx.core as mx
-import mlx.nn as nn
-
-from exo.shared.constants import EXO_MODELS_DIR
-
-
-class MockLayer(nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-        self.custom_attr = "test_value"
-        self.use_sliding = True
-
-    def __call__(self, x: mx.array, *args: object, **kwargs: object) -> mx.array:
-        return x * 2
-
-
-@dataclass(frozen=True)
-class PipelineTestConfig:
-    model_path: Path
-    total_layers: int
-    base_port: int
-    max_tokens: int
-
-
-def create_hostfile(world_size: int, base_port: int) -> tuple[str, list[str]]:
-    import json
-    import tempfile
-
-    hosts = [f"127.0.0.1:{base_port + i}" for i in range(world_size)]
-
-    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
-        json.dump(hosts, f)
-        hostfile_path = f.name
-
-    return hostfile_path, hosts
-
-
-# Use GPT OSS 20b to test as it is a model with a lot of strange behaviour
-
-DEFAULT_GPT_OSS_CONFIG = PipelineTestConfig(
-    model_path=EXO_MODELS_DIR / "mlx-community--gpt-oss-20b-MXFP4-Q8",
-    total_layers=24,
-    base_port=29600,
-    max_tokens=200,
-)
-
-
-def run_gpt_oss_pipeline_device(
-    rank: int,
-    world_size: int,
-    hostfile_path: str,
-    model_path: Path,
-    layer_splits: list[tuple[int, int]],
-    prompt_tokens: int,
-    prefill_step_size: int,
-    result_queue: Any,  # pyright: ignore[reportAny]
-    max_tokens: int = 200,
-) -> None:
-    import os
-    import traceback
-
-    os.environ["MLX_HOSTFILE"] = hostfile_path
-    os.environ["MLX_RANK"] = str(rank)
-
-    import mlx.core as mlx_core
-    from mlx_lm import load, stream_generate
-
-    from exo.shared.types.memory import Memory
-    from exo.shared.types.models import ModelId, ModelMetadata
-    from exo.shared.types.worker.shards import PipelineShardMetadata
-    from exo.worker.engines.mlx.auto_parallel import pipeline_auto_parallel
-
-    try:
-        group = mlx_core.distributed.init(backend="ring", strict=True)
-
-        model, tokenizer = load(str(model_path))
-
-        # Generate a prompt of exact token length
-        base_text = "The quick brown fox jumps over the lazy dog. "
-        base_tokens = tokenizer.encode(base_text)
-        base_len = len(base_tokens)
-
-        # Build prompt with approximate target length
-        repeats = (prompt_tokens // base_len) + 2
-        long_text = base_text * repeats
-        tokens = tokenizer.encode(long_text)
-        # Truncate to exact target length
-        tokens = tokens[:prompt_tokens]
-        prompt_text = tokenizer.decode(tokens)
-
-        formatted_prompt = tokenizer.apply_chat_template(
-            [{"role": "user", "content": prompt_text}],
-            tokenize=False,
-            add_generation_prompt=True,
-        )
-
-        start_layer, end_layer = layer_splits[rank]
-
-        shard_meta = PipelineShardMetadata(
-            model_meta=ModelMetadata(
-                model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
-                pretty_name="GPT-OSS 20B",
-                storage_size=Memory.from_gb(12),
-                n_layers=24,
-                hidden_size=2880,
-                supports_tensor=False,
-            ),
-            device_rank=rank,
-            world_size=world_size,
-            start_layer=start_layer,
-            end_layer=end_layer,
-            n_layers=24,
-        )
-
-        model = pipeline_auto_parallel(model, group, shard_meta)
-
-        # Barrier before generation
-        barrier = mlx_core.distributed.all_sum(mlx_core.array([1.0]), group=group)
-        mlx_core.eval(barrier)
-
-        generated_text = ""
-        for response in stream_generate(
-            model=model,
-            tokenizer=tokenizer,
-            prompt=formatted_prompt,
-            max_tokens=max_tokens,
-            prefill_step_size=prefill_step_size,
-        ):
-            generated_text += response.text
-
-        result_queue.put((rank, True, generated_text))  # pyright: ignore[reportAny]
-
-    except Exception as e:
-        result_queue.put((rank, False, f"{e}\n{traceback.format_exc()}"))  # pyright: ignore[reportAny]
-
-
-def run_gpt_oss_tensor_parallel_device(
-    rank: int,
-    world_size: int,
-    hostfile_path: str,
-    model_path: Path,
-    prompt_tokens: int,
-    prefill_step_size: int,
-    result_queue: Any,  # pyright: ignore[reportAny]
-    max_tokens: int = 10,
-) -> None:
-    import os
-    import traceback
-
-    os.environ["MLX_HOSTFILE"] = hostfile_path
-    os.environ["MLX_RANK"] = str(rank)
-
-    import mlx.core as mlx_core
-    from mlx_lm import load, stream_generate
-
-    from exo.worker.engines.mlx.auto_parallel import tensor_auto_parallel
-
-    try:
-        group = mlx_core.distributed.init(backend="ring", strict=True)
-
-        model, tokenizer = load(str(model_path))
-
-        base_text = "The quick brown fox jumps over the lazy dog. "
-        base_tokens = tokenizer.encode(base_text)
-        base_len = len(base_tokens)
-
-        repeats = (prompt_tokens // base_len) + 2
-        long_text = base_text * repeats
-        tokens = tokenizer.encode(long_text)
-        tokens = tokens[:prompt_tokens]
-        prompt_text = tokenizer.decode(tokens)
-
-        formatted_prompt = tokenizer.apply_chat_template(
-            [{"role": "user", "content": prompt_text}],
-            tokenize=False,
-            add_generation_prompt=True,
-        )
-
-        model = tensor_auto_parallel(model, group)
-
-        barrier = mlx_core.distributed.all_sum(mlx_core.array([1.0]), group=group)
-        mlx_core.eval(barrier)
-
-        generated_text = ""
-        for response in stream_generate(
-            model=model,
-            tokenizer=tokenizer,
-            prompt=formatted_prompt,
-            max_tokens=max_tokens,
-            prefill_step_size=prefill_step_size,
-        ):
-            generated_text += response.text
-
-        result_queue.put((rank, True, generated_text))  # pyright: ignore[reportAny]
-
-    except Exception as e:
-        result_queue.put((rank, False, f"{e}\n{traceback.format_exc()}"))  # pyright: ignore[reportAny]

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

@@ -1,137 +0,0 @@
-import multiprocessing as mp
-from typing import Any
-
-import mlx.core as mx
-import pytest
-
-from exo.worker.engines.mlx.auto_parallel import (
-    CustomMlxLayer,
-    PipelineFirstLayer,
-    PipelineLastLayer,
-)
-from exo.worker.tests.unittests.test_mlx.conftest import MockLayer
-
-
-def run_pipeline_device(
-    rank: int,
-    world_size: int,
-    hostfile_path: str,
-    result_queue: Any,  # pyright: ignore[reportAny]
-) -> None:
-    import os
-
-    os.environ["MLX_HOSTFILE"] = hostfile_path
-    os.environ["MLX_RANK"] = str(rank)
-
-    import mlx.core as mlx_core
-    import mlx.nn as mlx_nn
-
-    class MockLayerInner(mlx_nn.Module):
-        def __init__(self) -> None:
-            super().__init__()
-            self.custom_attr = "test_value"
-
-        def __call__(
-            self, x: mlx_core.array, *args: object, **kwargs: object
-        ) -> mlx_core.array:
-            return x * 2
-
-    try:
-        group = mlx_core.distributed.init(backend="ring", strict=True)
-
-        mock = MockLayerInner()
-        first = PipelineFirstLayer(mock, r=rank, group=group)
-        composed = PipelineLastLayer(first, r=rank, s=world_size, group=group)
-
-        x = mlx_core.ones((1, 4))
-        result = composed(x)
-        mlx_core.eval(result)
-
-        success = result.shape == x.shape
-        result_queue.put((rank, success, result))  # pyright: ignore[reportAny]
-    except Exception as e:
-        result_queue.put((rank, False, str(e)))  # pyright: ignore[reportAny]
-
-
-def test_single_wrapper_delegates_attributes() -> None:
-    mock = MockLayer()
-    wrapped = CustomMlxLayer(mock)
-
-    assert wrapped.custom_attr == "test_value"  # type: ignore[attr-defined]
-    assert wrapped.use_sliding is True  # type: ignore[attr-defined]
-
-
-def test_composed_wrappers_delegate_attributes() -> None:
-    mock = MockLayer()
-    group = mx.distributed.init()
-
-    first = PipelineFirstLayer(mock, r=0, group=group)
-    composed = PipelineLastLayer(first, r=0, s=1, group=group)
-
-    assert composed.custom_attr == "test_value"  # type: ignore[attr-defined]
-    assert composed.use_sliding is True  # type: ignore[attr-defined]
-
-
-def test_missing_attribute_raises() -> None:
-    mock = MockLayer()
-    wrapped = CustomMlxLayer(mock)
-
-    with pytest.raises(AttributeError):
-        _ = wrapped.nonexistent_attr  # type: ignore[attr-defined]
-
-
-def test_composed_call_works() -> None:
-    import json
-    import os
-    import tempfile
-
-    ctx = mp.get_context("spawn")
-
-    world_size = 2
-    base_port = 29500
-
-    hosts = [f"127.0.0.1:{base_port + i}" for i in range(world_size)]
-
-    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
-        json.dump(hosts, f)
-        hostfile_path = f.name
-
-    try:
-        result_queue: Any = ctx.Queue()
-
-        processes: list[Any] = []
-        for rank in range(world_size):
-            p = ctx.Process(
-                target=run_pipeline_device,
-                args=(rank, world_size, hostfile_path, result_queue),
-            )
-            p.start()
-            processes.append(p)
-
-        for p in processes:  # pyright: ignore[reportAny]
-            p.join(timeout=10)  # pyright: ignore[reportAny]
-
-        results: dict[int, Any] = {}
-        errors: dict[int, str] = {}
-        while not result_queue.empty():  # pyright: ignore[reportAny]
-            rank, success, value = result_queue.get()  # pyright: ignore[reportAny]
-            if success:
-                results[rank] = value
-            else:
-                errors[rank] = value
-
-        assert len(results) == world_size, (
-            f"Expected {world_size} results, got {len(results)}. Errors: {errors}"
-        )
-
-        for rank in range(world_size):
-            assert rank in results, (
-                f"Device {rank} failed: {errors.get(rank, 'unknown')}"
-            )
-            result_array = results[rank]
-            # Both devices see the final result (4.0) after all_gather
-            assert (result_array == 4.0).all(), (
-                f"Device {rank}: expected 4.0, got {result_array}"
-            )
-    finally:
-        os.unlink(hostfile_path)

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/utils/__init__.py

@@ -1,15 +1,6 @@
-from .profile import (
-    IdentityMetrics,
-    start_polling_identity_metrics,
-    start_polling_memory_metrics,
-    start_polling_network_metrics,
-    start_polling_system_metrics,
-)
+from .profile import start_polling_memory_metrics, start_polling_node_metrics
 
 __all__ = [
-    "IdentityMetrics",
-    "start_polling_identity_metrics",
+    "start_polling_node_metrics",
     "start_polling_memory_metrics",
-    "start_polling_network_metrics",
-    "start_polling_system_metrics",
 ]

src/exo/worker/utils/profile.py

@@ -1,7 +1,6 @@
 import asyncio
 import os
 import platform
-from dataclasses import dataclass
 from typing import Any, Callable, Coroutine
 
 import anyio
@@ -10,7 +9,7 @@ from loguru import logger
 from exo.shared.types.memory import Memory
 from exo.shared.types.profiling import (
     MemoryPerformanceProfile,
-    NetworkInterfaceInfo,
+    NodePerformanceProfile,
     SystemPerformanceProfile,
 )
 
@@ -28,13 +27,6 @@ from .system_info import (
 )
 
 
-@dataclass(frozen=True)
-class IdentityMetrics:
-    model_id: str
-    chip_id: str
-    friendly_name: str
-
-
 async def get_metrics_async() -> Metrics | None:
     """Return detailed Metrics on macOS or a minimal fallback elsewhere."""
 
@@ -75,73 +67,48 @@ async def start_polling_memory_metrics(
             await anyio.sleep(poll_interval_s)
 
 
-async def start_polling_identity_metrics(
-    callback: Callable[[IdentityMetrics], Coroutine[Any, Any, None]],
-    *,
-    poll_interval_s: float = 30.0,
-) -> None:
-    """Continuously poll and emit identity metrics at 30s intervals."""
-    while True:
-        try:
-            model_id, chip_id = await get_model_and_chip()
-            friendly_name = await get_friendly_name()
-            await callback(
-                IdentityMetrics(
-                    model_id=model_id,
-                    chip_id=chip_id,
-                    friendly_name=friendly_name,
-                )
-            )
-        except Exception as e:
-            logger.opt(exception=e).error("Failed to emit identity metrics")
-        finally:
-            await anyio.sleep(poll_interval_s)
-
-
-async def start_polling_system_metrics(
-    callback: Callable[[SystemPerformanceProfile], Coroutine[Any, Any, None]],
-    *,
-    poll_interval_s: float = 1.0,
-) -> None:
-    """Continuously poll and emit system metrics (GPU, temp, power) at 1s intervals."""
+async def start_polling_node_metrics(
+    callback: Callable[[NodePerformanceProfile], Coroutine[Any, Any, None]],
+):
+    poll_interval_s = 1.0
     while True:
         try:
             metrics = await get_metrics_async()
             if metrics is None:
                 return
 
+            network_interfaces = get_network_interfaces()
+            # these awaits could be joined but realistically they should be cached
+            model_id, chip_id = await get_model_and_chip()
+            friendly_name = await get_friendly_name()
+
+            # do the memory profile last to get a fresh reading to not conflict with the other memory profiling loop
+            memory_profile = get_memory_profile()
+
             await callback(
-                SystemPerformanceProfile(
-                    gpu_usage=metrics.gpu_usage[1],
-                    temp=metrics.temp.gpu_temp_avg,
-                    sys_power=metrics.sys_power,
-                    pcpu_usage=metrics.pcpu_usage[1],
-                    ecpu_usage=metrics.ecpu_usage[1],
-                    ane_power=metrics.ane_power,
+                NodePerformanceProfile(
+                    model_id=model_id,
+                    chip_id=chip_id,
+                    friendly_name=friendly_name,
+                    network_interfaces=network_interfaces,
+                    memory=memory_profile,
+                    system=SystemPerformanceProfile(
+                        gpu_usage=metrics.gpu_usage[1],
+                        temp=metrics.temp.gpu_temp_avg,
+                        sys_power=metrics.sys_power,
+                        pcpu_usage=metrics.pcpu_usage[1],
+                        ecpu_usage=metrics.ecpu_usage[1],
+                        ane_power=metrics.ane_power,
+                    ),
                 )
             )
+
         except asyncio.TimeoutError:
             logger.warning(
-                "[system_monitor] Operation timed out after 30s, skipping this cycle."
+                "[resource_monitor] Operation timed out after 30s, skipping this cycle."
             )
         except MacMonError as e:
-            logger.opt(exception=e).error("System Monitor encountered error")
+            logger.opt(exception=e).error("Resource Monitor encountered error")
             return
         finally:
             await anyio.sleep(poll_interval_s)
-
-
-async def start_polling_network_metrics(
-    callback: Callable[[list[NetworkInterfaceInfo]], Coroutine[Any, Any, None]],
-    *,
-    poll_interval_s: float = 30.0,
-) -> None:
-    """Continuously poll and emit network interface info at 30s intervals."""
-    while True:
-        try:
-            network_interfaces = get_network_interfaces()
-            await callback(network_interfaces)
-        except Exception as e:
-            logger.opt(exception=e).error("Network Monitor encountered error")
-        finally:
-            await anyio.sleep(poll_interval_s)

tests/headless_runner.py

@@ -124,7 +124,7 @@ 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,
@@ -134,15 +134,15 @@ 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)
             },
@@ -213,7 +213,7 @@ async def jaccl_backend(test: Tests):
 
 
 def jaccl_instance(test: Tests, iid: InstanceId, hn: str):
-    meta = MODEL_CARDS[test.model_id].metadata
+    card = MODEL_CARDS[test.model_id]
     world_size = len(test.devs)
 
     return MlxJacclInstance(
@@ -228,12 +228,12 @@ def jaccl_instance(test: Tests, iid: InstanceId, hn: str):
             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)
             },