Add support for Ollama API

## Motivation

TB has better bandwidth and latency than ethernet. We should prioritise
TB5 where possible. This drastically improves distributed image
generation performance.

## Test Plan

### Manual Testing
Saw on the dashboard that TB (169.254) addresses were prioritised.

Tested that image models scale much better.

### Automated Testing
No regression on Kimi K2.5

dashboard/src/lib/components/MetaInstanceCard.svelte

@@ -1,232 +0,0 @@
-<script lang="ts">
-  import type {
-    MetaInstance,
-    MetaInstanceStatus,
-    NodeInfo,
-  } from "$lib/stores/app.svelte";
-  import {
-    getMetaInstanceStatus,
-    getMetaInstanceBackingNodes,
-    topologyData,
-  } from "$lib/stores/app.svelte";
-
-  interface Props {
-    metaInstance: MetaInstance;
-    onDelete?: (metaInstanceId: string) => void;
-  }
-
-  let { metaInstance, onDelete }: Props = $props();
-
-  const status: MetaInstanceStatus = $derived(
-    getMetaInstanceStatus(metaInstance),
-  );
-  const backingNodeIds: string[] = $derived(
-    getMetaInstanceBackingNodes(metaInstance),
-  );
-
-  const statusConfig = $derived.by(() => {
-    switch (status) {
-      case "active":
-        return {
-          label: "ACTIVE",
-          dotClass: "bg-green-400",
-          borderClass:
-            "border-green-500/30 border-l-green-400",
-          cornerClass: "border-green-500/50",
-          glowClass: "shadow-[0_0_6px_rgba(74,222,128,0.4)]",
-          animate: false,
-        };
-      case "provisioning":
-        return {
-          label: "PROVISIONING",
-          dotClass: "bg-yellow-400",
-          borderClass:
-            "border-exo-yellow/30 border-l-yellow-400",
-          cornerClass: "border-yellow-500/50",
-          glowClass: "shadow-[0_0_6px_rgba(250,204,21,0.4)]",
-          animate: true,
-        };
-      case "error":
-        return {
-          label: "ERROR",
-          dotClass: "bg-red-400",
-          borderClass: "border-red-500/30 border-l-red-400",
-          cornerClass: "border-red-500/50",
-          glowClass: "shadow-[0_0_6px_rgba(248,113,113,0.4)]",
-          animate: false,
-        };
-    }
-  });
-
-  function getNodeName(nodeId: string): string {
-    const topo = topologyData();
-    if (!topo?.nodes) return nodeId.slice(0, 8);
-    const node = topo.nodes[nodeId];
-    return node?.friendly_name || node?.system_info?.model_id || nodeId.slice(0, 8);
-  }
-
-  function formatModelId(modelId: string): string {
-    // Show just the model name part after the org prefix
-    const parts = modelId.split("/");
-    return parts.length > 1 ? parts[parts.length - 1] : modelId;
-  }
-
-  function handleDelete() {
-    if (
-      onDelete &&
-      confirm(
-        `Delete meta-instance for ${formatModelId(metaInstance.modelId)}?`,
-      )
-    ) {
-      onDelete(metaInstance.metaInstanceId);
-    }
-  }
-</script>
-
-<div class="relative group">
-  <!-- Corner accents -->
-  <div
-    class="absolute -top-px -left-px w-2 h-2 border-l border-t {statusConfig.cornerClass}"
-  ></div>
-  <div
-    class="absolute -top-px -right-px w-2 h-2 border-r border-t {statusConfig.cornerClass}"
-  ></div>
-  <div
-    class="absolute -bottom-px -left-px w-2 h-2 border-l border-b {statusConfig.cornerClass}"
-  ></div>
-  <div
-    class="absolute -bottom-px -right-px w-2 h-2 border-r border-b {statusConfig.cornerClass}"
-  ></div>
-
-  <div
-    class="bg-exo-dark-gray/60 border border-l-2 {statusConfig.borderClass} p-3"
-  >
-    <!-- Header: Status + Delete -->
-    <div class="flex justify-between items-start mb-2 pl-2">
-      <div class="flex items-center gap-2">
-        <div
-          class="w-1.5 h-1.5 {statusConfig.dotClass} rounded-full {statusConfig.glowClass} {statusConfig.animate
-            ? 'animate-pulse'
-            : ''}"
-        ></div>
-        <span
-          class="text-xs font-mono tracking-[0.15em] uppercase {status === 'active'
-            ? 'text-green-400'
-            : status === 'error'
-              ? 'text-red-400'
-              : 'text-yellow-400'}"
-        >
-          {statusConfig.label}
-        </span>
-      </div>
-      <button
-        onclick={handleDelete}
-        class="text-xs px-2 py-1 font-mono tracking-wider uppercase border border-red-500/30 text-red-400 hover:bg-red-500/20 hover:text-red-400 hover:border-red-500/50 transition-all duration-200 cursor-pointer"
-      >
-        DELETE
-      </button>
-    </div>
-
-    <!-- Model Info -->
-    <div class="pl-2 space-y-1">
-      <div class="text-exo-yellow text-xs font-mono tracking-wide truncate">
-        {metaInstance.modelId}
-      </div>
-
-      <!-- Sharding + Runtime badges -->
-      <div class="flex items-center gap-2">
-        <span
-          class="inline-flex items-center px-1.5 py-0.5 text-[10px] font-mono tracking-wider uppercase border border-white/10 text-white/50"
-        >
-          {metaInstance.sharding}
-        </span>
-        <span
-          class="inline-flex items-center px-1.5 py-0.5 text-[10px] font-mono tracking-wider uppercase border border-white/10 text-white/50"
-        >
-          {metaInstance.instanceMeta}
-        </span>
-        {#if metaInstance.minNodes > 1}
-          <span
-            class="inline-flex items-center px-1.5 py-0.5 text-[10px] font-mono tracking-wider uppercase border border-white/10 text-white/50"
-          >
-            {metaInstance.minNodes}+ nodes
-          </span>
-        {/if}
-      </div>
-
-      <!-- Node Assignments (when active) -->
-      {#if backingNodeIds.length > 0}
-        <div class="flex items-center gap-1.5 mt-1">
-          <svg
-            class="w-3 h-3 text-green-400/70 flex-shrink-0"
-            viewBox="0 0 24 24"
-            fill="none"
-            stroke="currentColor"
-            stroke-width="2"
-          >
-            <path
-              d="M22 12h-4l-3 9L9 3l-3 9H2"
-              stroke-linecap="round"
-              stroke-linejoin="round"
-            />
-          </svg>
-          <span class="text-white/60 text-xs font-mono truncate">
-            {backingNodeIds.map((id) => getNodeName(id)).join(", ")}
-          </span>
-        </div>
-      {/if}
-
-      <!-- Pinned nodes constraint -->
-      {#if metaInstance.nodeIds && metaInstance.nodeIds.length > 0}
-        <div class="flex items-center gap-1.5">
-          <svg
-            class="w-3 h-3 text-white/40 flex-shrink-0"
-            viewBox="0 0 24 24"
-            fill="none"
-            stroke="currentColor"
-            stroke-width="2"
-          >
-            <rect x="3" y="11" width="18" height="11" rx="2" ry="2" />
-            <path d="M7 11V7a5 5 0 0 1 10 0v4" />
-          </svg>
-          <span class="text-white/40 text-[11px] font-mono">
-            Pinned: {metaInstance.nodeIds
-              .map((id) => getNodeName(id))
-              .join(", ")}
-          </span>
-        </div>
-      {/if}
-
-      <!-- Error details -->
-      {#if metaInstance.placementError}
-        <div
-          class="mt-1.5 p-2 bg-red-500/5 border border-red-500/15 rounded-sm"
-        >
-          <div class="text-red-400 text-[11px] font-mono leading-relaxed">
-            {metaInstance.placementError}
-          </div>
-        </div>
-      {/if}
-
-      <!-- Retry counter -->
-      {#if metaInstance.consecutiveFailures > 0}
-        <div class="flex items-center gap-1.5 mt-1">
-          <svg
-            class="w-3 h-3 text-yellow-500/60 flex-shrink-0"
-            viewBox="0 0 24 24"
-            fill="none"
-            stroke="currentColor"
-            stroke-width="2"
-          >
-            <polyline points="23 4 23 10 17 10" />
-            <path d="M20.49 15a9 9 0 1 1-2.12-9.36L23 10" />
-          </svg>
-          <span class="text-yellow-500/60 text-[11px] font-mono">
-            {metaInstance.consecutiveFailures} consecutive
-            failure{metaInstance.consecutiveFailures !== 1 ? "s" : ""}
-          </span>
-        </div>
-      {/if}
-    </div>
-  </div>
-</div>

dashboard/src/lib/components/index.ts

@@ -11,5 +11,4 @@ export { default as FamilySidebar } from "./FamilySidebar.svelte";
 export { default as HuggingFaceResultItem } from "./HuggingFaceResultItem.svelte";
 export { default as ModelFilterPopover } from "./ModelFilterPopover.svelte";
 export { default as ModelPickerGroup } from "./ModelPickerGroup.svelte";
-export { default as MetaInstanceCard } from "./MetaInstanceCard.svelte";
 export { default as ModelPickerModal } from "./ModelPickerModal.svelte";

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

@@ -72,23 +72,8 @@ export interface Instance {
     runnerToShard?: Record<string, unknown>;
     nodeToRunner?: Record<string, string>;
   };
-  metaInstanceId?: string | null;
 }
 
-export interface MetaInstance {
-  metaInstanceId: string;
-  modelId: string;
-  sharding: "Pipeline" | "Tensor";
-  instanceMeta: "MlxRing" | "MlxJaccl";
-  minNodes: number;
-  nodeIds: string[] | null;
-  placementError: string | null;
-  consecutiveFailures: number;
-  lastFailureError: string | null;
-}
-
-export type MetaInstanceStatus = "active" | "provisioning" | "error";
-
 // Granular node state types from the new state structure
 interface RawNodeIdentity {
   modelId?: string;
@@ -238,7 +223,6 @@ interface RawStateResponse {
       MlxJacclInstance?: Instance;
     }
   >;
-  metaInstances?: Record<string, MetaInstance>;
   runners?: Record<string, unknown>;
   downloads?: Record<string, unknown[]>;
   // New granular node state fields
@@ -266,6 +250,11 @@ interface RawStateResponse {
   >;
   // Thunderbolt bridge cycles (nodes with bridge enabled forming loops)
   thunderboltBridgeCycles?: string[][];
+  // Disk usage per node
+  nodeDisk?: Record<
+    string,
+    { total: { inBytes: number }; available: { inBytes: number } }
+  >;
 }
 
 export interface MessageAttachment {
@@ -549,7 +538,6 @@ class AppStore {
   // Topology state
   topologyData = $state<TopologyData | null>(null);
   instances = $state<Record<string, unknown>>({});
-  metaInstances = $state<Record<string, MetaInstance>>({});
   runners = $state<Record<string, unknown>>({});
   downloads = $state<Record<string, unknown[]>>({});
   nodeDisk = $state<
@@ -1285,9 +1273,6 @@ class AppStore {
         this.instances = data.instances;
         this.refreshConversationModelFromInstances();
       }
-      if (data.metaInstances) {
-        this.metaInstances = data.metaInstances;
-      }
       if (data.runners) {
         this.runners = data.runners;
       }
@@ -1313,79 +1298,6 @@ class AppStore {
     }
   }
 
-  async createMetaInstance(
-    modelId: string,
-    sharding: "Pipeline" | "Tensor" = "Pipeline",
-    instanceMeta: "MlxRing" | "MlxJaccl" = "MlxRing",
-    minNodes: number = 1,
-    nodeIds: string[] | null = null,
-  ) {
-    try {
-      const response = await fetch("/meta_instance", {
-        method: "POST",
-        headers: { "Content-Type": "application/json" },
-        body: JSON.stringify({
-          model_id: modelId,
-          sharding,
-          instance_meta: instanceMeta,
-          min_nodes: minNodes,
-          node_ids: nodeIds,
-        }),
-      });
-      if (!response.ok) {
-        console.error("Failed to create meta-instance:", response.status);
-      }
-      await this.fetchState();
-    } catch (error) {
-      console.error("Error creating meta-instance:", error);
-    }
-  }
-
-  async deleteMetaInstance(metaInstanceId: string) {
-    try {
-      const response = await fetch(`/meta_instance/${metaInstanceId}`, {
-        method: "DELETE",
-        headers: { "Content-Type": "application/json" },
-      });
-      if (!response.ok) {
-        console.error("Failed to delete meta-instance:", response.status);
-      }
-      await this.fetchState();
-    } catch (error) {
-      console.error("Error deleting meta-instance:", error);
-    }
-  }
-
-  getMetaInstanceStatus(
-    metaInstance: MetaInstance,
-  ): MetaInstanceStatus {
-    // Check if any running instance is bound to this meta-instance
-    for (const instanceWrapper of Object.values(this.instances)) {
-      if (!instanceWrapper || typeof instanceWrapper !== "object") continue;
-      const keys = Object.keys(instanceWrapper as Record<string, unknown>);
-      if (keys.length !== 1) continue;
-      const inner = (instanceWrapper as Record<string, unknown>)[keys[0]];
-      if (inner && typeof inner === "object" && (inner as Instance).metaInstanceId === metaInstance.metaInstanceId) {
-        return "active";
-      }
-    }
-    if (metaInstance.placementError) return "error";
-    return "provisioning";
-  }
-
-  getMetaInstanceBackingNodes(metaInstance: MetaInstance): string[] {
-    for (const instanceWrapper of Object.values(this.instances)) {
-      if (!instanceWrapper || typeof instanceWrapper !== "object") continue;
-      const keys = Object.keys(instanceWrapper as Record<string, unknown>);
-      if (keys.length !== 1) continue;
-      const inner = (instanceWrapper as Record<string, unknown>)[keys[0]] as Instance;
-      if (inner?.metaInstanceId === metaInstance.metaInstanceId && inner?.shardAssignments?.nodeToRunner) {
-        return Object.keys(inner.shardAssignments.nodeToRunner);
-      }
-    }
-    return [];
-  }
-
   async fetchPlacementPreviews(modelId: string, showLoading = true) {
     if (!modelId) return;
 
@@ -3247,7 +3159,6 @@ export const totalTokens = () => appStore.totalTokens;
 export const prefillProgress = () => appStore.prefillProgress;
 export const topologyData = () => appStore.topologyData;
 export const instances = () => appStore.instances;
-export const metaInstances = () => appStore.metaInstances;
 export const runners = () => appStore.runners;
 export const downloads = () => appStore.downloads;
 export const nodeDisk = () => appStore.nodeDisk;
@@ -3336,21 +3247,6 @@ export const setChatSidebarVisible = (visible: boolean) =>
   appStore.setChatSidebarVisible(visible);
 export const refreshState = () => appStore.fetchState();
 
-// Meta-instance actions
-export const createMetaInstance = (
-  modelId: string,
-  sharding?: "Pipeline" | "Tensor",
-  instanceMeta?: "MlxRing" | "MlxJaccl",
-  minNodes?: number,
-  nodeIds?: string[] | null,
-) => appStore.createMetaInstance(modelId, sharding, instanceMeta, minNodes, nodeIds);
-export const deleteMetaInstance = (metaInstanceId: string) =>
-  appStore.deleteMetaInstance(metaInstanceId);
-export const getMetaInstanceStatus = (metaInstance: MetaInstance) =>
-  appStore.getMetaInstanceStatus(metaInstance);
-export const getMetaInstanceBackingNodes = (metaInstance: MetaInstance) =>
-  appStore.getMetaInstanceBackingNodes(metaInstance);
-
 // Node identities (for OS version mismatch detection)
 export const nodeIdentities = () => appStore.nodeIdentities;
 

dashboard/src/routes/+page.svelte

@@ -47,14 +47,10 @@
     thunderboltBridgeCycles,
     nodeThunderboltBridge,
     nodeIdentities,
-    metaInstances,
-    deleteMetaInstance,
     type DownloadProgress,
     type PlacementPreview,
-    type MetaInstance,
   } from "$lib/stores/app.svelte";
   import HeaderNav from "$lib/components/HeaderNav.svelte";
-  import MetaInstanceCard from "$lib/components/MetaInstanceCard.svelte";
   import { fade, fly } from "svelte/transition";
   import { cubicInOut } from "svelte/easing";
   import { onMount } from "svelte";
@@ -71,8 +67,6 @@
   const loadingPreviews = $derived(isLoadingPreviews());
   const debugEnabled = $derived(debugMode());
   const topologyOnlyEnabled = $derived(topologyOnlyMode());
-  const metaInstanceData = $derived(metaInstances());
-  const metaInstanceCount = $derived(Object.keys(metaInstanceData).length);
   const sidebarVisible = $derived(chatSidebarVisible());
   const tbBridgeCycles = $derived(thunderboltBridgeCycles());
   const tbBridgeData = $derived(nodeThunderboltBridge());
@@ -864,10 +858,8 @@
     if (!progress || typeof progress !== "object") return null;
 
     const prog = progress as Record<string, unknown>;
-    const totalBytes = getBytes(prog.total_bytes ?? prog.totalBytes);
-    const downloadedBytes = getBytes(
-      prog.downloaded_bytes ?? prog.downloadedBytes,
-    );
+    const totalBytes = getBytes(prog.total);
+    const downloadedBytes = getBytes(prog.downloaded);
     const speed = (prog.speed as number) ?? 0;
     const completedFiles =
       (prog.completed_files as number) ?? (prog.completedFiles as number) ?? 0;
@@ -880,8 +872,8 @@
     for (const [fileName, fileData] of Object.entries(filesObj)) {
       if (!fileData || typeof fileData !== "object") continue;
       const fd = fileData as Record<string, unknown>;
-      const fTotal = getBytes(fd.total_bytes ?? fd.totalBytes);
-      const fDownloaded = getBytes(fd.downloaded_bytes ?? fd.downloadedBytes);
+      const fTotal = getBytes(fd.total);
+      const fDownloaded = getBytes(fd.downloaded);
       files.push({
         name: fileName,
         totalBytes: fTotal,
@@ -1270,7 +1262,6 @@
     if (typeof value === "number") return value;
     if (value && typeof value === "object") {
       const v = value as Record<string, unknown>;
-      if (typeof v.in_bytes === "number") return v.in_bytes;
       if (typeof v.inBytes === "number") return v.inBytes;
     }
     return 0;
@@ -3062,39 +3053,6 @@
             </div>
           {/if}
 
-          <!-- Meta-Instances Panel -->
-          {#if metaInstanceCount > 0}
-            <div class="p-4 flex-shrink-0 border-t border-exo-yellow/10">
-              <!-- Panel Header -->
-              <div class="flex items-center gap-2 mb-4">
-                <div
-                  class="w-2 h-2 border border-purple-400/60 rotate-45"
-                ></div>
-                <h3
-                  class="text-xs text-purple-400 font-mono tracking-[0.2em] uppercase"
-                >
-                  Meta-Instances
-                </h3>
-                <div
-                  class="flex-1 h-px bg-gradient-to-r from-purple-400/30 to-transparent"
-                ></div>
-                <span class="text-[10px] text-white/40 font-mono"
-                  >{metaInstanceCount}</span
-                >
-              </div>
-
-              <div class="space-y-3">
-                {#each Object.entries(metaInstanceData) as [id, mi]}
-                  <MetaInstanceCard
-                    metaInstance={mi}
-                    onDelete={(metaInstanceId) =>
-                      deleteMetaInstance(metaInstanceId)}
-                  />
-                {/each}
-              </div>
-            </div>
-          {/if}
-
           <!-- Models Panel - Scrollable -->
           <div class="p-4 flex-1 overflow-y-auto">
             <!-- Panel Header -->
@@ -3917,34 +3875,6 @@
                 </div>
               </div>
             {/if}
-
-            <!-- Meta-Instances Section (chat sidebar) -->
-            {#if metaInstanceCount > 0}
-              <div class="p-4 border-t border-exo-yellow/10">
-                <div class="flex items-center gap-2 mb-4">
-                  <div
-                    class="w-2 h-2 border border-purple-400/60 rotate-45"
-                  ></div>
-                  <h3
-                    class="text-xs text-purple-400 font-mono tracking-[0.2em] uppercase"
-                  >
-                    Meta-Instances
-                  </h3>
-                  <div
-                    class="flex-1 h-px bg-gradient-to-r from-purple-400/30 to-transparent"
-                  ></div>
-                </div>
-                <div class="space-y-3">
-                  {#each Object.entries(metaInstanceData) as [id, mi]}
-                    <MetaInstanceCard
-                      metaInstance={mi}
-                      onDelete={(metaInstanceId) =>
-                        deleteMetaInstance(metaInstanceId)}
-                    />
-                  {/each}
-                </div>
-              </div>
-            {/if}
           </aside>
         {/if}
       </div>

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

@@ -74,7 +74,6 @@
     if (typeof value === "number") return value;
     if (value && typeof value === "object") {
       const v = value as Record<string, unknown>;
-      if (typeof v.in_bytes === "number") return v.in_bytes;
       if (typeof v.inBytes === "number") return v.inBytes;
     }
     return 0;
@@ -231,23 +230,14 @@
             undefined;
           let cell: CellStatus;
           if (tag === "DownloadCompleted") {
-            const totalBytes = getBytes(
-              payload.total_bytes ?? payload.totalBytes,
-            );
+            const totalBytes = getBytes(payload.total);
             cell = { kind: "completed", totalBytes, modelDirectory };
           } else if (tag === "DownloadOngoing") {
             const rawProgress =
               payload.download_progress ?? payload.downloadProgress ?? {};
             const prog = rawProgress as Record<string, unknown>;
-            const totalBytes = getBytes(
-              prog.total_bytes ??
-                prog.totalBytes ??
-                payload.total_bytes ??
-                payload.totalBytes,
-            );
-            const downloadedBytes = getBytes(
-              prog.downloaded_bytes ?? prog.downloadedBytes,
-            );
+            const totalBytes = getBytes(prog.total ?? payload.total);
+            const downloadedBytes = getBytes(prog.downloaded);
             const speed = (prog.speed as number) ?? 0;
             const etaMs =
               (prog.eta_ms as number) ?? (prog.etaMs as number) ?? 0;

src/exo/download/coordinator.py

@@ -80,7 +80,7 @@ class DownloadCoordinator:
             completed = DownloadCompleted(
                 shard_metadata=callback_shard,
                 node_id=self.node_id,
-                total_bytes=progress.total_bytes,
+                total=progress.total,
                 model_directory=self._model_dir(model_id),
             )
             self.download_status[model_id] = completed
@@ -203,7 +203,7 @@ class DownloadCoordinator:
             completed = DownloadCompleted(
                 shard_metadata=shard,
                 node_id=self.node_id,
-                total_bytes=initial_progress.total_bytes,
+                total=initial_progress.total,
                 model_directory=self._model_dir(model_id),
             )
             self.download_status[model_id] = completed
@@ -332,13 +332,13 @@ class DownloadCoordinator:
                         status: DownloadProgress = DownloadCompleted(
                             node_id=self.node_id,
                             shard_metadata=progress.shard,
-                            total_bytes=progress.total_bytes,
+                            total=progress.total,
                             model_directory=self._model_dir(
                                 progress.shard.model_card.model_id
                             ),
                         )
                     elif progress.status in ["in_progress", "not_started"]:
-                        if progress.downloaded_bytes_this_session.in_bytes == 0:
+                        if progress.downloaded_this_session.in_bytes == 0:
                             status = DownloadPending(
                                 node_id=self.node_id,
                                 shard_metadata=progress.shard,

src/exo/download/download_utils.py

@@ -80,9 +80,9 @@ def map_repo_file_download_progress_to_download_progress_data(
     repo_file_download_progress: RepoFileDownloadProgress,
 ) -> DownloadProgressData:
     return DownloadProgressData(
-        downloaded_bytes=repo_file_download_progress.downloaded,
-        downloaded_bytes_this_session=repo_file_download_progress.downloaded_this_session,
-        total_bytes=repo_file_download_progress.total,
+        downloaded=repo_file_download_progress.downloaded,
+        downloaded_this_session=repo_file_download_progress.downloaded_this_session,
+        total=repo_file_download_progress.total,
         completed_files=1 if repo_file_download_progress.status == "complete" else 0,
         total_files=1,
         speed=repo_file_download_progress.speed,
@@ -95,9 +95,9 @@ def map_repo_download_progress_to_download_progress_data(
     repo_download_progress: RepoDownloadProgress,
 ) -> DownloadProgressData:
     return DownloadProgressData(
-        total_bytes=repo_download_progress.total_bytes,
-        downloaded_bytes=repo_download_progress.downloaded_bytes,
-        downloaded_bytes_this_session=repo_download_progress.downloaded_bytes_this_session,
+        total=repo_download_progress.total,
+        downloaded=repo_download_progress.downloaded,
+        downloaded_this_session=repo_download_progress.downloaded_this_session,
         completed_files=repo_download_progress.completed_files,
         total_files=repo_download_progress.total_files,
         speed=repo_download_progress.overall_speed,
@@ -578,19 +578,20 @@ def calculate_repo_progress(
     file_progress: dict[str, RepoFileDownloadProgress],
     all_start_time: float,
 ) -> RepoDownloadProgress:
-    all_total_bytes = sum((p.total.in_bytes for p in file_progress.values()), 0)
-    all_downloaded_bytes = sum(
-        (p.downloaded.in_bytes for p in file_progress.values()), 0
+    all_total = sum((p.total for p in file_progress.values()), Memory.from_bytes(0))
+    all_downloaded = sum(
+        (p.downloaded for p in file_progress.values()), Memory.from_bytes(0)
     )
-    all_downloaded_bytes_this_session = sum(
-        (p.downloaded_this_session.in_bytes for p in file_progress.values()), 0
+    all_downloaded_this_session = sum(
+        (p.downloaded_this_session for p in file_progress.values()),
+        Memory.from_bytes(0),
     )
     elapsed_time = time.time() - all_start_time
     all_speed = (
-        all_downloaded_bytes_this_session / elapsed_time if elapsed_time > 0 else 0
+        all_downloaded_this_session.in_bytes / elapsed_time if elapsed_time > 0 else 0
     )
     all_eta = (
-        timedelta(seconds=(all_total_bytes - all_downloaded_bytes) / all_speed)
+        timedelta(seconds=(all_total - all_downloaded).in_bytes / all_speed)
         if all_speed > 0
         else timedelta(seconds=0)
     )
@@ -609,11 +610,9 @@ def calculate_repo_progress(
             [p for p in file_progress.values() if p.downloaded == p.total]
         ),
         total_files=len(file_progress),
-        downloaded_bytes=Memory.from_bytes(all_downloaded_bytes),
-        downloaded_bytes_this_session=Memory.from_bytes(
-            all_downloaded_bytes_this_session
-        ),
-        total_bytes=Memory.from_bytes(all_total_bytes),
+        downloaded=all_downloaded,
+        downloaded_this_session=all_downloaded_this_session,
+        total=all_total,
         overall_speed=all_speed,
         overall_eta=all_eta,
         status=status,

src/exo/download/shard_downloader.py

@@ -107,9 +107,9 @@ NOOP_DOWNLOAD_PROGRESS = RepoDownloadProgress(
     ),
     completed_files=0,
     total_files=0,
-    downloaded_bytes=Memory.from_bytes(0),
-    downloaded_bytes_this_session=Memory.from_bytes(0),
-    total_bytes=Memory.from_bytes(0),
+    downloaded=Memory.from_bytes(0),
+    downloaded_this_session=Memory.from_bytes(0),
+    total=Memory.from_bytes(0),
     overall_speed=0,
     overall_eta=timedelta(seconds=0),
     status="complete",

src/exo/master/adapters/ollama.py

@@ -0,0 +1,456 @@
+from __future__ import annotations
+
+import json
+from collections.abc import AsyncGenerator
+from typing import Any
+
+from exo.shared.types.chunks import (
+    ErrorChunk,
+    PrefillProgressChunk,
+    TokenChunk,
+    ToolCallChunk,
+)
+from exo.shared.types.common import CommandId
+from exo.shared.types.ollama_api import (
+    OllamaChatRequest,
+    OllamaChatResponse,
+    OllamaDoneReason,
+    OllamaGenerateRequest,
+    OllamaGenerateResponse,
+    OllamaMessage,
+    OllamaToolCall,
+    OllamaToolFunction,
+)
+from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
+
+
+def _map_done_reason(
+    finish_reason: str | None,
+) -> OllamaDoneReason | None:
+    if finish_reason is None:
+        return None
+    if finish_reason == "stop":
+        return "stop"
+    if finish_reason == "length":
+        return "length"
+    if finish_reason in ("tool_calls", "function_call"):
+        return "tool_call"
+    if finish_reason == "error":
+        return "error"
+    return "stop"
+
+
+def _try_parse_json(value: str) -> dict[str, Any] | str:
+    try:
+        return json.loads(value)  # type: ignore
+    except json.JSONDecodeError:
+        return value
+
+
+def _build_tool_calls(chunk: ToolCallChunk) -> list[OllamaToolCall]:
+    tool_calls: list[OllamaToolCall] = []
+    for index, tool in enumerate(chunk.tool_calls):
+        # tool.arguments is always str; try to parse as JSON dict for Ollama format
+        arguments: dict[str, Any] | str = _try_parse_json(tool.arguments)
+        tool_calls.append(
+            OllamaToolCall(
+                id=tool.id,
+                type="function",
+                function=OllamaToolFunction(
+                    name=tool.name, arguments=arguments, index=index
+                ),
+            )
+        )
+    return tool_calls
+
+
+def _get_usage(
+    chunk: TokenChunk | ToolCallChunk,
+) -> tuple[int | None, int | None]:
+    """Extract (prompt_eval_count, eval_count) from a chunk."""
+    if chunk.usage is not None:
+        return (chunk.usage.prompt_tokens, chunk.usage.completion_tokens)
+    if chunk.stats is not None:
+        return (chunk.stats.prompt_tokens, chunk.stats.generation_tokens)
+    return (None, None)
+
+
+def ollama_request_to_text_generation(
+    request: OllamaChatRequest,
+) -> TextGenerationTaskParams:
+    """Convert Ollama chat request to exo's internal text generation format."""
+    instructions: str | None = None
+    input_messages: list[InputMessage] = []
+    chat_template_messages: list[dict[str, Any]] = []
+    tool_message_index = 0
+
+    for msg in request.messages:
+        content = msg.content or ""
+
+        if msg.role == "system":
+            if instructions is None:
+                instructions = content
+            else:
+                instructions = f"{instructions}\n{content}"
+            chat_template_messages.append({"role": "system", "content": content})
+            continue
+
+        if msg.role in ("user", "assistant") and (
+            msg.content is not None or msg.thinking is not None or msg.tool_calls
+        ):
+            input_messages.append(InputMessage(role=msg.role, content=content))
+
+        dumped: dict[str, Any] = {"role": msg.role, "content": content}
+        if msg.thinking is not None:
+            dumped["thinking"] = msg.thinking
+        if msg.tool_calls is not None:
+            tool_calls_list: list[dict[str, Any]] = []
+            for tc in msg.tool_calls:
+                function: dict[str, Any] = {
+                    "name": tc.function.name,
+                    "arguments": (
+                        json.dumps(tc.function.arguments)
+                        if isinstance(tc.function.arguments, dict)
+                        else tc.function.arguments
+                    ),
+                }
+                if tc.function.index is not None:
+                    function["index"] = tc.function.index
+                tool_call: dict[str, Any] = {"function": function}
+                if tc.id is not None:
+                    tool_call["id"] = tc.id
+                if tc.type is not None:
+                    tool_call["type"] = tc.type
+                tool_calls_list.append(tool_call)
+            dumped["tool_calls"] = tool_calls_list
+        if msg.name is not None:
+            dumped["name"] = msg.name
+        if msg.role == "tool":
+            tool_message_index += 1
+            tool_call_id = msg.tool_name or msg.name or f"tool_{tool_message_index}"
+            dumped["tool_call_id"] = tool_call_id
+            if msg.tool_name is not None:
+                dumped["tool_name"] = msg.tool_name
+        chat_template_messages.append(dumped)
+
+    options = request.options
+    return TextGenerationTaskParams(
+        model=request.model,
+        input=input_messages
+        if input_messages
+        else [InputMessage(role="user", content="")],
+        instructions=instructions,
+        max_output_tokens=options.num_predict if options else None,
+        temperature=options.temperature if options else None,
+        top_p=options.top_p if options else None,
+        top_k=options.top_k if options else None,
+        stop=options.stop if options else None,
+        seed=options.seed if options else None,
+        stream=request.stream,
+        tools=request.tools,
+        enable_thinking=request.think,
+        chat_template_messages=chat_template_messages
+        if chat_template_messages
+        else None,
+    )
+
+
+async def generate_ollama_chat_stream(
+    _command_id: CommandId,
+    chunk_stream: AsyncGenerator[
+        ErrorChunk | ToolCallChunk | TokenChunk | PrefillProgressChunk, None
+    ],
+) -> AsyncGenerator[str, None]:
+    """Generate streaming responses in Ollama format (newline-delimited JSON)."""
+    thinking_parts: list[str] = []
+
+    async for chunk in chunk_stream:
+        match chunk:
+            case PrefillProgressChunk():
+                continue
+
+            case ErrorChunk():
+                error_response = OllamaChatResponse(
+                    model=str(chunk.model),
+                    message=OllamaMessage(
+                        role="assistant", content=chunk.error_message
+                    ),
+                    done=True,
+                    done_reason="error",
+                )
+                yield f"{error_response.model_dump_json(exclude_none=True)}\n"
+                return
+
+            case ToolCallChunk():
+                prompt_eval, eval_count = _get_usage(chunk)
+                response = OllamaChatResponse(
+                    model=str(chunk.model),
+                    message=OllamaMessage(
+                        role="assistant",
+                        content="",
+                        tool_calls=_build_tool_calls(chunk),
+                        thinking="".join(thinking_parts) if thinking_parts else None,
+                    ),
+                    done=True,
+                    done_reason="tool_call",
+                    prompt_eval_count=prompt_eval,
+                    eval_count=eval_count,
+                )
+                yield f"{response.model_dump_json(exclude_none=True)}\n"
+                return
+
+            case TokenChunk():
+                done = chunk.finish_reason is not None
+
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                    response = OllamaChatResponse(
+                        model=str(chunk.model),
+                        message=OllamaMessage(
+                            role="assistant", content="", thinking=chunk.text
+                        ),
+                        done=False,
+                    )
+                    yield f"{response.model_dump_json(exclude_none=True)}\n"
+                elif done:
+                    prompt_eval, eval_count = _get_usage(chunk)
+                    response = OllamaChatResponse(
+                        model=str(chunk.model),
+                        message=OllamaMessage(
+                            role="assistant",
+                            content=chunk.text,
+                        ),
+                        done=True,
+                        done_reason=_map_done_reason(chunk.finish_reason),
+                        prompt_eval_count=prompt_eval,
+                        eval_count=eval_count,
+                    )
+                    yield f"{response.model_dump_json(exclude_none=True)}\n"
+                else:
+                    response = OllamaChatResponse(
+                        model=str(chunk.model),
+                        message=OllamaMessage(role="assistant", content=chunk.text),
+                        done=False,
+                    )
+                    yield f"{response.model_dump_json(exclude_none=True)}\n"
+
+                if done:
+                    return
+
+
+async def collect_ollama_chat_response(
+    _command_id: CommandId,
+    chunk_stream: AsyncGenerator[
+        ErrorChunk | ToolCallChunk | TokenChunk | PrefillProgressChunk, None
+    ],
+) -> AsyncGenerator[str]:
+    """Collect streaming chunks into a single non-streaming Ollama response.
+
+    Returns an AsyncGenerator[str] (single yield) for consistency with FastAPI
+    StreamingResponse cancellation handling.
+    """
+    text_parts: list[str] = []
+    thinking_parts: list[str] = []
+    tool_calls: list[OllamaToolCall] = []
+    model: str | None = None
+    finish_reason: str | None = None
+    prompt_eval_count: int | None = None
+    eval_count: int | None = None
+
+    async for chunk in chunk_stream:
+        match chunk:
+            case PrefillProgressChunk():
+                continue
+
+            case ErrorChunk():
+                raise ValueError(chunk.error_message or "Internal server error")
+
+            case TokenChunk():
+                if model is None:
+                    model = str(chunk.model)
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                else:
+                    text_parts.append(chunk.text)
+                if chunk.finish_reason is not None:
+                    finish_reason = chunk.finish_reason
+                    prompt_eval_count, eval_count = _get_usage(chunk)
+
+            case ToolCallChunk():
+                if model is None:
+                    model = str(chunk.model)
+                tool_calls.extend(_build_tool_calls(chunk))
+                finish_reason = chunk.finish_reason
+                prompt_eval_count, eval_count = _get_usage(chunk)
+
+    combined_text = "".join(text_parts)
+    combined_thinking = "".join(thinking_parts) if thinking_parts else None
+    assert model is not None
+
+    yield OllamaChatResponse(
+        model=model,
+        message=OllamaMessage(
+            role="assistant",
+            content=combined_text,
+            thinking=combined_thinking,
+            tool_calls=tool_calls if tool_calls else None,
+        ),
+        done=True,
+        done_reason=_map_done_reason(finish_reason),
+        prompt_eval_count=prompt_eval_count,
+        eval_count=eval_count,
+    ).model_dump_json(exclude_none=True)
+    return
+
+
+# ── /api/generate ──
+
+
+def ollama_generate_request_to_text_generation(
+    request: OllamaGenerateRequest,
+) -> TextGenerationTaskParams:
+    """Convert Ollama generate request to exo's internal text generation format."""
+    chat_template_messages: list[dict[str, Any]] = []
+    if request.system:
+        chat_template_messages.append({"role": "system", "content": request.system})
+    chat_template_messages.append({"role": "user", "content": request.prompt})
+
+    options = request.options
+    return TextGenerationTaskParams(
+        model=request.model,
+        input=[InputMessage(role="user", content=request.prompt)],
+        instructions=request.system,
+        max_output_tokens=options.num_predict if options else None,
+        temperature=options.temperature if options else None,
+        top_p=options.top_p if options else None,
+        top_k=options.top_k if options else None,
+        stop=options.stop if options else None,
+        seed=options.seed if options else None,
+        stream=request.stream,
+        enable_thinking=request.think,
+        chat_template_messages=chat_template_messages
+        if chat_template_messages
+        else None,
+    )
+
+
+async def generate_ollama_generate_stream(
+    _command_id: CommandId,
+    chunk_stream: AsyncGenerator[
+        ErrorChunk | ToolCallChunk | TokenChunk | PrefillProgressChunk, None
+    ],
+) -> AsyncGenerator[str, None]:
+    """Generate streaming responses for /api/generate in Ollama NDJSON format."""
+    thinking_parts: list[str] = []
+
+    async for chunk in chunk_stream:
+        match chunk:
+            case PrefillProgressChunk():
+                continue
+
+            case ErrorChunk():
+                resp = OllamaGenerateResponse(
+                    model=str(chunk.model),
+                    response="",
+                    done=True,
+                    done_reason="error",
+                )
+                yield f"{resp.model_dump_json(exclude_none=True)}\n"
+                return
+
+            case ToolCallChunk():
+                # generate endpoint doesn't support tools; emit as done
+                prompt_eval, eval_count = _get_usage(chunk)
+                resp = OllamaGenerateResponse(
+                    model=str(chunk.model),
+                    response="",
+                    done=True,
+                    done_reason="stop",
+                    prompt_eval_count=prompt_eval,
+                    eval_count=eval_count,
+                )
+                yield f"{resp.model_dump_json(exclude_none=True)}\n"
+                return
+
+            case TokenChunk():
+                done = chunk.finish_reason is not None
+
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                    resp = OllamaGenerateResponse(
+                        model=str(chunk.model),
+                        response="",
+                        thinking=chunk.text,
+                        done=False,
+                    )
+                    yield f"{resp.model_dump_json(exclude_none=True)}\n"
+                elif done:
+                    prompt_eval, eval_count = _get_usage(chunk)
+                    resp = OllamaGenerateResponse(
+                        model=str(chunk.model),
+                        response=chunk.text,
+                        done=True,
+                        done_reason=_map_done_reason(chunk.finish_reason),
+                        prompt_eval_count=prompt_eval,
+                        eval_count=eval_count,
+                    )
+                    yield f"{resp.model_dump_json(exclude_none=True)}\n"
+                else:
+                    resp = OllamaGenerateResponse(
+                        model=str(chunk.model),
+                        response=chunk.text,
+                        done=False,
+                    )
+                    yield f"{resp.model_dump_json(exclude_none=True)}\n"
+
+                if done:
+                    return
+
+
+async def collect_ollama_generate_response(
+    _command_id: CommandId,
+    chunk_stream: AsyncGenerator[
+        ErrorChunk | ToolCallChunk | TokenChunk | PrefillProgressChunk, None
+    ],
+) -> AsyncGenerator[str]:
+    """Collect chunks into a single non-streaming /api/generate response."""
+    text_parts: list[str] = []
+    thinking_parts: list[str] = []
+    model: str | None = None
+    finish_reason: str | None = None
+    prompt_eval_count: int | None = None
+    eval_count: int | None = None
+
+    async for chunk in chunk_stream:
+        match chunk:
+            case PrefillProgressChunk():
+                continue
+            case ErrorChunk():
+                raise ValueError(chunk.error_message or "Internal server error")
+            case TokenChunk():
+                if model is None:
+                    model = str(chunk.model)
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                else:
+                    text_parts.append(chunk.text)
+                if chunk.finish_reason is not None:
+                    finish_reason = chunk.finish_reason
+                    prompt_eval_count, eval_count = _get_usage(chunk)
+            case ToolCallChunk():
+                if model is None:
+                    model = str(chunk.model)
+                finish_reason = chunk.finish_reason
+                prompt_eval_count, eval_count = _get_usage(chunk)
+
+    assert model is not None
+    yield OllamaGenerateResponse(
+        model=model,
+        response="".join(text_parts),
+        thinking="".join(thinking_parts) if thinking_parts else None,
+        done=True,
+        done_reason=_map_done_reason(finish_reason),
+        prompt_eval_count=prompt_eval_count,
+        eval_count=eval_count,
+    ).model_dump_json(exclude_none=True)
+    return

src/exo/master/api.py

@@ -32,6 +32,14 @@ from exo.master.adapters.claude import (
     collect_claude_response,
     generate_claude_stream,
 )
+from exo.master.adapters.ollama import (
+    collect_ollama_chat_response,
+    collect_ollama_generate_response,
+    generate_ollama_chat_stream,
+    generate_ollama_generate_stream,
+    ollama_generate_request_to_text_generation,
+    ollama_request_to_text_generation,
+)
 from exo.master.adapters.responses import (
     collect_responses_response,
     generate_responses_stream,
@@ -141,6 +149,19 @@ from exo.shared.types.events import (
     TracesMerged,
 )
 from exo.shared.types.memory import Memory
+from exo.shared.types.ollama_api import (
+    OllamaChatRequest,
+    OllamaChatResponse,
+    OllamaGenerateRequest,
+    OllamaGenerateResponse,
+    OllamaModelDetails,
+    OllamaModelTag,
+    OllamaPsModel,
+    OllamaPsResponse,
+    OllamaShowRequest,
+    OllamaShowResponse,
+    OllamaTagsResponse,
+)
 from exo.shared.types.openai_responses import (
     ResponsesRequest,
     ResponsesResponse,
@@ -300,6 +321,20 @@ class API:
         self.app.get("/images/{image_id}")(self.get_image)
         self.app.post("/v1/messages", response_model=None)(self.claude_messages)
         self.app.post("/v1/responses", response_model=None)(self.openai_responses)
+        # Ollama API — health checks (must be before static files mount)
+        self.app.head("/")(self._ollama_root)
+        self.app.head("/api/version")(self.ollama_version)
+        # Ollama API
+        self.app.post("/api/chat", response_model=None)(self.ollama_chat)
+        self.app.post("/api/api/chat", response_model=None)(self.ollama_chat)
+        self.app.post("/api/v1/chat", response_model=None)(self.ollama_chat)
+        self.app.post("/api/generate", response_model=None)(self.ollama_generate)
+        self.app.get("/api/tags")(self.ollama_tags)
+        self.app.get("/api/api/tags")(self.ollama_tags)
+        self.app.get("/api/v1/tags")(self.ollama_tags)
+        self.app.post("/api/show")(self.ollama_show)
+        self.app.get("/api/ps")(self.ollama_ps)
+        self.app.get("/api/version")(self.ollama_version)
         self.app.get("/state")(lambda: self.state)
         self.app.get("/events")(self.stream_events)
         self.app.post("/download/start")(self.start_download)
@@ -1293,6 +1328,158 @@ class API:
                 media_type="application/json",
             )
 
+    async def _ollama_root(self) -> JSONResponse:
+        """Respond to HEAD / from Ollama CLI connectivity checks."""
+        return JSONResponse(content="Ollama is running")
+
+    async def ollama_chat(
+        self, request: Request
+    ) -> OllamaChatResponse | StreamingResponse:
+        """Ollama Chat API — accepts JSON regardless of Content-Type."""
+        body = await request.body()
+        payload = OllamaChatRequest.model_validate_json(body)
+        task_params = ollama_request_to_text_generation(payload)
+        resolved_model = await self._resolve_and_validate_text_model(
+            ModelId(task_params.model)
+        )
+        task_params = task_params.model_copy(update={"model": resolved_model})
+
+        command = TextGeneration(task_params=task_params)
+        await self._send(command)
+
+        if payload.stream:
+            return StreamingResponse(
+                generate_ollama_chat_stream(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="application/x-ndjson",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
+            )
+        else:
+            return StreamingResponse(
+                collect_ollama_chat_response(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="application/json",
+            )
+
+    async def ollama_generate(
+        self, request: Request
+    ) -> OllamaGenerateResponse | StreamingResponse:
+        """Ollama Generate API — accepts JSON regardless of Content-Type."""
+        body = await request.body()
+        payload = OllamaGenerateRequest.model_validate_json(body)
+        task_params = ollama_generate_request_to_text_generation(payload)
+        resolved_model = await self._resolve_and_validate_text_model(
+            ModelId(task_params.model)
+        )
+        task_params = task_params.model_copy(update={"model": resolved_model})
+
+        command = TextGeneration(task_params=task_params)
+        await self._send(command)
+
+        if payload.stream:
+            return StreamingResponse(
+                generate_ollama_generate_stream(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="application/x-ndjson",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
+            )
+        else:
+            return StreamingResponse(
+                collect_ollama_generate_response(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="application/json",
+            )
+
+    async def ollama_tags(self) -> OllamaTagsResponse:
+        """Returns list of models in Ollama tags format. We return the downloaded ones only."""
+
+        def none_if_empty(value: str) -> str | None:
+            return value or None
+
+        downloaded_model_ids: set[str] = set()
+        for node_downloads in self.state.downloads.values():
+            for dl in node_downloads:
+                if isinstance(dl, DownloadCompleted):
+                    downloaded_model_ids.add(dl.shard_metadata.model_card.model_id)
+
+        cards = [
+            c for c in await get_model_cards() if c.model_id in downloaded_model_ids
+        ]
+
+        now = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+        return OllamaTagsResponse(
+            models=[
+                OllamaModelTag(
+                    name=str(card.model_id),
+                    model=str(card.model_id),
+                    modified_at=now,
+                    size=card.storage_size.in_bytes,
+                    digest="sha256:000000000000",
+                    details=OllamaModelDetails(
+                        family=none_if_empty(card.family),
+                        quantization_level=none_if_empty(card.quantization),
+                    ),
+                )
+                for card in cards
+            ]
+        )
+
+    async def ollama_show(self, request: Request) -> OllamaShowResponse:
+        """Returns model information in Ollama show format."""
+        body = await request.body()
+        payload = OllamaShowRequest.model_validate_json(body)
+        try:
+            card = await ModelCard.load(ModelId(payload.name))
+        except Exception as exc:
+            raise HTTPException(
+                status_code=404, detail=f"Model not found: {payload.name}"
+            ) from exc
+
+        return OllamaShowResponse(
+            details=OllamaModelDetails(
+                family=card.family or None,
+                quantization_level=card.quantization or None,
+            ),
+        )
+
+    async def ollama_ps(self) -> OllamaPsResponse:
+        """Returns list of running models (active instances)."""
+        models: list[OllamaPsModel] = []
+        seen: set[str] = set()
+        for instance in self.state.instances.values():
+            model_id = str(instance.shard_assignments.model_id)
+            if model_id in seen:
+                continue
+            seen.add(model_id)
+            models.append(
+                OllamaPsModel(
+                    name=model_id,
+                    model=model_id,
+                    size=0,
+                )
+            )
+        return OllamaPsResponse(models=models)
+
+    async def ollama_version(self) -> dict[str, str]:
+        """Returns version information for Ollama API compatibility."""
+        return {"version": "exo v1.0"}
+
     def _calculate_total_available_memory(self) -> Memory:
         """Calculate total available memory across all nodes in bytes."""
         total_available = Memory()
@@ -1322,7 +1509,7 @@ class API:
                     name=card.model_id.short(),
                     description="",
                     tags=[],
-                    storage_size_megabytes=int(card.storage_size.in_mb),
+                    storage_size_megabytes=card.storage_size.in_mb,
                     supports_tensor=card.supports_tensor,
                     tasks=[task.value for task in card.tasks],
                     is_custom=is_custom_card(card.model_id),

src/exo/master/placement_utils.py

@@ -102,22 +102,21 @@ def _allocate_and_validate_layers(
     layer_allocations = allocate_layers_proportionally(
         total_layers=model_card.n_layers,
         memory_fractions=[
-            node_memory[node_id].ram_available.in_bytes / total_memory.in_bytes
-            for node_id in node_ids
+            node_memory[node_id].ram_available / total_memory for node_id in node_ids
         ],
     )
 
-    total_storage_bytes = model_card.storage_size.in_bytes
+    total_storage = model_card.storage_size
     total_layers = model_card.n_layers
     for i, node_id in enumerate(node_ids):
         node_layers = layer_allocations[i]
-        required_memory = (total_storage_bytes * node_layers) // total_layers
-        available_memory = node_memory[node_id].ram_available.in_bytes
+        required_memory = (total_storage * node_layers) // total_layers
+        available_memory = node_memory[node_id].ram_available
         if required_memory > available_memory:
             raise ValueError(
                 f"Node {i} ({node_id}) has insufficient memory: "
-                f"requires {required_memory / (1024**3):.2f} GB for {node_layers} layers, "
-                f"but only has {available_memory / (1024**3):.2f} GB available"
+                f"requires {required_memory.in_gb:.2f} GB for {node_layers} layers, "
+                f"but only has {available_memory.in_gb:.2f} GB available"
             )
 
     return layer_allocations
@@ -342,6 +341,7 @@ def _find_ip_prioritised(
     other_node_id: NodeId,
     cycle_digraph: Topology,
     node_network: Mapping[NodeId, NodeNetworkInfo],
+    ring: bool,
 ) -> str | None:
     """Find an IP address between nodes with prioritization.
 
@@ -354,13 +354,27 @@ def _find_ip_prioritised(
     ip_to_type = {
         iface.ip_address: iface.interface_type for iface in other_network.interfaces
     }
-    priority = {
-        "ethernet": 0,
-        "wifi": 1,
-        "unknown": 2,
-        "maybe_ethernet": 3,
-        "thunderbolt": 4,
-    }
+
+    # Ring should prioritise fastest connection. As a best-effort, we prioritise TB.
+    # TODO: Profile and get actual connection speeds.
+    if ring:
+        priority = {
+            "thunderbolt": 0,
+            "maybe_ethernet": 1,
+            "ethernet": 2,
+            "wifi": 3,
+            "unknown": 4,
+        }
+
+    # RDMA prefers ethernet coordinator
+    else:
+        priority = {
+            "ethernet": 0,
+            "wifi": 1,
+            "unknown": 2,
+            "maybe_ethernet": 3,
+            "thunderbolt": 4,
+        }
     return min(ips, key=lambda ip: priority.get(ip_to_type.get(ip, "unknown"), 2))
 
 
@@ -400,7 +414,7 @@ def get_mlx_ring_hosts_by_node(
                 continue
 
             connection_ip = _find_ip_prioritised(
-                node_id, other_node_id, cycle_digraph, node_network
+                node_id, other_node_id, cycle_digraph, node_network, ring=True
             )
             if connection_ip is None:
                 raise ValueError(
@@ -431,7 +445,9 @@ def get_mlx_jaccl_coordinators(
         if n == coordinator:
             return "0.0.0.0"
 
-        ip = _find_ip_prioritised(n, coordinator, cycle_digraph, node_network)
+        ip = _find_ip_prioritised(
+            n, coordinator, cycle_digraph, node_network, ring=False
+        )
         if ip is not None:
             return ip
 

src/exo/master/tests/test_placement.py

@@ -80,8 +80,8 @@ def test_get_instance_placements_create_instance(
 ):
     # arrange
     model_card.n_layers = total_layers
-    model_card.storage_size.in_bytes = sum(
-        available_memory
+    model_card.storage_size = Memory.from_bytes(
+        sum(available_memory)
     )  # make it exactly fit across all nodes
     topology = Topology()
 
@@ -349,7 +349,7 @@ def test_tensor_rdma_backend_connectivity_matrix(
     # arrange
     topology = Topology()
     model_card.n_layers = 12
-    model_card.storage_size.in_bytes = 1500
+    model_card.storage_size = Memory.from_bytes(1500)
 
     node_a = NodeId()
     node_b = NodeId()

src/exo/shared/tests/test_apply/test_apply_node_download.py

@@ -14,7 +14,7 @@ def test_apply_node_download_progress():
     event = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard1,
-        total_bytes=Memory(),
+        total=Memory(),
     )
 
     new_state = apply_node_download_progress(
@@ -30,12 +30,12 @@ def test_apply_two_node_download_progress():
     event1 = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard1,
-        total_bytes=Memory(),
+        total=Memory(),
     )
     event2 = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard2,
-        total_bytes=Memory(),
+        total=Memory(),
     )
     state = State(downloads={NodeId("node-1"): [event1]})
 

src/exo/shared/types/memory.py

@@ -1,10 +1,10 @@
 from math import ceil
-from typing import Self
+from typing import Self, overload
 
-from exo.utils.pydantic_ext import CamelCaseModel
+from exo.utils.pydantic_ext import FrozenModel
 
 
-class Memory(CamelCaseModel):
+class Memory(FrozenModel):
     in_bytes: int = 0
 
     @classmethod
@@ -33,12 +33,22 @@ class Memory(CamelCaseModel):
         return cls(in_bytes=round(val * 1024))
 
     @property
-    def in_mb(self) -> float:
-        """The approximate megabytes this memory represents. Setting this property rounds to the nearest byte."""
-        return self.in_bytes / (1024**2)
+    def in_mb(self) -> int:
+        """The approximate megabytes this memory represents, rounded to nearest MB. Setting this property rounds to the nearest byte."""
+        return round(self.in_bytes / (1024**2))
 
     @in_mb.setter
-    def in_mb(self, val: float):
+    def in_mb(self, val: int):
+        """Set the megabytes for this memory."""
+        self.in_bytes = val * (1024**2)
+
+    @property
+    def in_float_mb(self) -> float:
+        """The megabytes this memory represents as a float. Setting this property rounds to the nearest byte."""
+        return self.in_bytes / (1024**2)
+
+    @in_float_mb.setter
+    def in_float_mb(self, val: float):
         """Set the megabytes for this memory, rounded to the nearest byte."""
         self.in_bytes = round(val * (1024**2))
 
@@ -57,17 +67,85 @@ class Memory(CamelCaseModel):
         """The approximate gigabytes this memory represents."""
         return self.in_bytes / (1024**3)
 
-    def __add__(self, other: "Memory") -> "Memory":
-        return Memory.from_bytes(self.in_bytes + other.in_bytes)
+    def __add__(self, other: object) -> "Memory":
+        if isinstance(other, Memory):
+            return Memory.from_bytes(self.in_bytes + other.in_bytes)
+        return NotImplemented
 
-    def __lt__(self, other: Self) -> bool:
-        return self.in_bytes < other.in_bytes
+    def __radd__(self, other: object) -> "Memory":
+        if other == 0:
+            return self
+        return NotImplemented
 
-    def __le__(self, other: Self) -> bool:
-        return self.in_bytes <= other.in_bytes
+    def __sub__(self, other: object) -> "Memory":
+        if isinstance(other, Memory):
+            return Memory.from_bytes(self.in_bytes - other.in_bytes)
+        return NotImplemented
 
-    def __gt__(self, other: Self) -> bool:
-        return self.in_bytes > other.in_bytes
+    def __mul__(self, other: int | float):
+        return Memory.from_bytes(round(self.in_bytes * other))
 
-    def __ge__(self, other: Self) -> bool:
-        return self.in_bytes >= other.in_bytes
+    def __rmul__(self, other: int | float):
+        return self * other
+
+    @overload
+    def __truediv__(self, other: "Memory") -> float: ...
+    @overload
+    def __truediv__(self, other: int) -> "Memory": ...
+    @overload
+    def __truediv__(self, other: float) -> "Memory": ...
+    def __truediv__(self, other: object) -> "Memory | float":
+        if isinstance(other, Memory):
+            return self.in_bytes / other.in_bytes
+        if isinstance(other, (int, float)):
+            return Memory.from_bytes(round(self.in_bytes / other))
+        return NotImplemented
+
+    def __floordiv__(self, other: object) -> "Memory":
+        if isinstance(other, (int, float)):
+            return Memory.from_bytes(int(self.in_bytes // other))
+        return NotImplemented
+
+    def __lt__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes < other.in_bytes
+        return NotImplemented
+
+    def __le__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes <= other.in_bytes
+        return NotImplemented
+
+    def __gt__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes > other.in_bytes
+        return NotImplemented
+
+    def __ge__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes >= other.in_bytes
+        return NotImplemented
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes == other.in_bytes
+        return NotImplemented
+
+    def __repr__(self) -> str:
+        return f"Memory.from_bytes({self.in_bytes})"
+
+    def __str__(self) -> str:
+        if self.in_gb > 2:
+            val = self.in_gb
+            unit = "GiB"
+        elif self.in_mb > 2:
+            val = self.in_mb
+            unit = "MiB"
+        elif self.in_kb > 3:
+            val = self.in_kb
+            unit = "KiB"
+        else:
+            val = self.in_bytes
+            unit = "B"
+
+        return f"{val:.2f} {unit}".rstrip("0").rstrip(".") + f" {unit}"

src/exo/shared/types/ollama_api.py

@@ -0,0 +1,147 @@
+from __future__ import annotations
+
+import time
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+from exo.shared.models.model_cards import ModelId
+
+# https://github.com/ollama/ollama/blob/main/docs/api.md
+
+OllamaRole = Literal["system", "user", "assistant", "tool"]
+OllamaDoneReason = Literal["stop", "length", "tool_call", "error"]
+
+
+class OllamaToolFunction(BaseModel, frozen=True):
+    name: str
+    arguments: dict[str, Any] | str
+    index: int | None = None
+
+
+class OllamaToolCall(BaseModel, frozen=True):
+    id: str | None = None
+    type: Literal["function"] | None = None
+    function: OllamaToolFunction
+
+
+class OllamaMessage(BaseModel, frozen=True):
+    role: OllamaRole
+    content: str | None = None
+    thinking: str | None = None
+    tool_calls: list[OllamaToolCall] | None = None
+    name: str | None = None
+    tool_name: str | None = None
+    images: list[str] | None = None
+
+
+class OllamaOptions(BaseModel, frozen=True):
+    num_predict: int | None = None
+    temperature: float | None = None
+    top_p: float | None = None
+    top_k: int | None = None
+    stop: str | list[str] | None = None
+    seed: int | None = None
+
+
+class OllamaChatRequest(BaseModel, frozen=True):
+    model: ModelId
+    messages: list[OllamaMessage]
+    stream: bool = True
+    options: OllamaOptions | None = None
+    tools: list[dict[str, Any]] | None = None
+    format: Literal["json"] | dict[str, Any] | None = None
+    keep_alive: str | int | None = None
+    think: bool | None = None
+
+
+class OllamaGenerateRequest(BaseModel, frozen=True):
+    model: ModelId
+    prompt: str = ""
+    system: str | None = None
+    stream: bool = True
+    options: OllamaOptions | None = None
+    format: Literal["json"] | dict[str, Any] | None = None
+    keep_alive: str | int | None = None
+    think: bool | None = None
+    raw: bool = False
+
+
+class OllamaGenerateResponse(BaseModel, frozen=True, strict=True):
+    model: str
+    created_at: str = Field(
+        default_factory=lambda: time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+    )
+    response: str
+    thinking: str | None = None
+    done: bool
+    done_reason: OllamaDoneReason | None = None
+    total_duration: int | None = None
+    load_duration: int | None = None
+    prompt_eval_count: int | None = None
+    prompt_eval_duration: int | None = None
+    eval_count: int | None = None
+    eval_duration: int | None = None
+
+
+class OllamaShowRequest(BaseModel, frozen=True):
+    name: str
+    verbose: bool | None = None
+
+
+class OllamaChatResponse(BaseModel, frozen=True, strict=True):
+    model: str
+    created_at: str = Field(
+        default_factory=lambda: time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+    )
+    message: OllamaMessage
+    done: bool
+    done_reason: OllamaDoneReason | None = None
+    total_duration: int | None = None
+    load_duration: int | None = None
+    prompt_eval_count: int | None = None
+    prompt_eval_duration: int | None = None
+    eval_count: int | None = None
+    eval_duration: int | None = None
+
+
+class OllamaModelDetails(BaseModel, frozen=True, strict=True):
+    format: str | None = None
+    family: str | None = None
+    parameter_size: str | None = None
+    quantization_level: str | None = None
+
+
+class OllamaModelTag(BaseModel, frozen=True, strict=True):
+    name: str
+    model: str | None = None
+    modified_at: str | None = None
+    size: int | None = None
+    digest: str | None = None
+    details: OllamaModelDetails | None = None
+
+
+class OllamaTagsResponse(BaseModel, frozen=True, strict=True):
+    models: list[OllamaModelTag]
+
+
+class OllamaShowResponse(BaseModel, frozen=True, strict=True):
+    modelfile: str | None = None
+    parameters: str | None = None
+    template: str | None = None
+    details: OllamaModelDetails | None = None
+    model_info: dict[str, Any] | None = None
+
+
+class OllamaPsModel(BaseModel, frozen=True, strict=True):
+    name: str
+    model: str
+    size: int
+    digest: str | None = None
+    details: OllamaModelDetails | None = None
+    expires_at: str | None = None
+    size_vram: int | None = None
+
+
+class OllamaPsResponse(BaseModel, frozen=True, strict=True):
+    models: list[OllamaPsModel]

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

@@ -10,9 +10,9 @@ from exo.utils.pydantic_ext import CamelCaseModel, TaggedModel
 
 
 class DownloadProgressData(CamelCaseModel):
-    total_bytes: Memory
-    downloaded_bytes: Memory
-    downloaded_bytes_this_session: Memory
+    total: Memory
+    downloaded: Memory
+    downloaded_this_session: Memory
 
     completed_files: int
     total_files: int
@@ -34,7 +34,7 @@ class DownloadPending(BaseDownloadProgress):
 
 
 class DownloadCompleted(BaseDownloadProgress):
-    total_bytes: Memory
+    total: Memory
 
 
 class DownloadFailed(BaseDownloadProgress):
@@ -86,9 +86,9 @@ class RepoDownloadProgress(BaseModel):
     shard: ShardMetadata
     completed_files: int
     total_files: int
-    downloaded_bytes: Memory
-    downloaded_bytes_this_session: Memory
-    total_bytes: Memory
+    downloaded: Memory
+    downloaded_this_session: Memory
+    total: Memory
     overall_speed: float
     overall_eta: timedelta
     status: Literal["not_started", "in_progress", "complete"]

src/exo/utils/channels.py

@@ -192,7 +192,13 @@ class MpReceiver[T]:
         try:
             return self.receive_nowait()
         except WouldBlock:
-            item = self._state.buffer.get()
+            try:
+                item = self._state.buffer.get()
+            except (TypeError, OSError):
+                # Queue pipe can get closed while we are blocked on get().
+                # The underlying connection._handle becomes None, causing
+                # TypeError in read(handle, remaining).
+                raise ClosedResourceError from None
             if isinstance(item, _MpEndOfStream):
                 self.close()
                 raise EndOfStream from None

src/exo/utils/info_gatherer/net_profile.py

@@ -108,7 +108,7 @@ async def check_reachable(
                 await send.send((target_ip, expected_node_id))
 
     async with (
-        httpx.AsyncClient(timeout=timeout, limits=limits) as client,
+        httpx.AsyncClient(timeout=timeout, limits=limits, verify=False) as client,
         create_task_group() as tg,
     ):
         for node_id in topology.list_nodes():

src/exo/worker/engines/image/generate.py

@@ -166,7 +166,7 @@ def generate_image(
                             else 0.0
                         )
 
-                        peak_memory_gb = mx.get_peak_memory() / (1024**3)
+                        peak_memory = Memory.from_bytes(mx.get_peak_memory())
 
                         stats = ImageGenerationStats(
                             seconds_per_step=seconds_per_step,
@@ -175,7 +175,7 @@ def generate_image(
                             num_images=num_images,
                             image_width=width,
                             image_height=height,
-                            peak_memory_usage=Memory.from_gb(peak_memory_gb),
+                            peak_memory_usage=peak_memory,
                         )
 
                     buffer = io.BytesIO()

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

@@ -22,7 +22,7 @@ from exo.worker.runner.bootstrap import logger
 # Fraction of device memory above which LRU eviction kicks in.
 # Smaller machines need more aggressive eviction.
 def _default_memory_threshold() -> float:
-    total_gb = psutil.virtual_memory().total / (1024**3)
+    total_gb = Memory.from_bytes(psutil.virtual_memory().total).in_gb
     if total_gb >= 128:
         return 0.85
     if total_gb >= 64:

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

@@ -232,11 +232,11 @@ def shard_and_load(
 
     # Estimate timeout based on model size (5x default for large queued workloads)
     base_timeout = float(os.environ.get("EXO_MODEL_LOAD_TIMEOUT", "300"))
-    model_size_gb = get_weights_size(shard_metadata).in_bytes / (1024**3)
-    timeout_seconds = base_timeout + model_size_gb
+    model_size = get_weights_size(shard_metadata)
+    timeout_seconds = base_timeout + model_size.in_gb
     logger.info(
         f"Evaluating model parameters with timeout of {timeout_seconds:.0f}s "
-        f"(model size: {model_size_gb:.1f}GB)"
+        f"(model size: {model_size.in_gb:.1f}GB)"
     )
 
     match shard_metadata:
@@ -642,18 +642,17 @@ def set_wired_limit_for_model(model_size: Memory):
     if not mx.metal.is_available():
         return
 
-    model_bytes = model_size.in_bytes
-    max_rec_size = int(mx.metal.device_info()["max_recommended_working_set_size"])
-    if model_bytes > 0.9 * max_rec_size:
-        model_mb = model_bytes // 2**20
-        max_rec_mb = max_rec_size // 2**20
+    max_rec_size = Memory.from_bytes(
+        int(mx.metal.device_info()["max_recommended_working_set_size"])
+    )
+    if model_size > 0.9 * max_rec_size:
         logger.warning(
-            f"Generating with a model that requires {model_mb} MB "
-            f"which is close to the maximum recommended size of {max_rec_mb} "
+            f"Generating with a model that requires {model_size.in_float_mb:.1f} MB "
+            f"which is close to the maximum recommended size of {max_rec_size.in_float_mb:.1f} "
             "MB. This can be slow. See the documentation for possible work-arounds: "
             "https://github.com/ml-explore/mlx-lm/tree/main#large-models"
         )
-    mx.set_wired_limit(max_rec_size)
+    mx.set_wired_limit(max_rec_size.in_bytes)
     logger.info(f"Wired limit set to {max_rec_size}.")
 
 

src/exo/worker/runner/runner.py

@@ -4,7 +4,7 @@ import resource
 import time
 from collections.abc import Generator
 from functools import cache
-from typing import Literal
+from typing import TYPE_CHECKING, Literal
 
 import mlx.core as mx
 from mlx_lm.models.deepseek_v32 import Model as DeepseekV32Model
@@ -588,6 +588,13 @@ def main(
                 case Shutdown():
                     current_status = RunnerShuttingDown()
                     logger.info("runner shutting down")
+                    if not TYPE_CHECKING:
+                        del inference_model, image_model, tokenizer, group
+                        mx.clear_cache()
+                        import gc
+
+                        gc.collect()
+
                     event_sender.send(
                         RunnerStatusUpdated(
                             runner_id=runner_id, runner_status=current_status
@@ -612,12 +619,8 @@ def main(
             event_sender.send(
                 RunnerStatusUpdated(runner_id=runner_id, runner_status=current_status)
             )
-            if isinstance(current_status, RunnerShutdown):
-                del inference_model, image_model, tokenizer, group
-                mx.clear_cache()
-                import gc
 
-                gc.collect()
+            if isinstance(current_status, RunnerShutdown):
                 break
 
 

src/exo/worker/runner/runner_supervisor.py

@@ -100,8 +100,8 @@ class RunnerSupervisor:
         logger.info("Runner supervisor shutting down")
         self._ev_recv.close()
         self._task_sender.close()
-        self._event_sender.close()
-        self._cancel_sender.send(TaskId("CANCEL_CURRENT_TASK"))
+        with contextlib.suppress(ClosedResourceError):
+            self._cancel_sender.send(TaskId("CANCEL_CURRENT_TASK"))
         self._cancel_sender.close()
         self.runner_process.join(5)
         if not self.runner_process.is_alive():
@@ -180,6 +180,7 @@ class RunnerSupervisor:
                 await self._check_runner(e)
                 for tid in self.pending:
                     self.pending[tid].set()
+        self._event_sender.close()
 
     def __del__(self) -> None:
         if self.runner_process.is_alive():
@@ -208,10 +209,15 @@ class RunnerSupervisor:
 
         logger.opt(exception=e).error(f"Runner terminated ({cause})")
 
-        await self._event_sender.send(
-            RunnerStatusUpdated(
-                runner_id=self.bound_instance.bound_runner_id,
-                runner_status=RunnerFailed(error_message=f"Terminated ({cause})"),
+        try:
+            await self._event_sender.send(
+                RunnerStatusUpdated(
+                    runner_id=self.bound_instance.bound_runner_id,
+                    runner_status=RunnerFailed(error_message=f"Terminated ({cause})"),
+                )
+            )
+        except (ClosedResourceError, BrokenResourceError):
+            logger.warning(
+                "Event sender already closed, unable to report runner failure"
             )
-        )
         self.shutdown()

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

@@ -90,14 +90,10 @@ def test_plan_loads_model_when_all_shards_downloaded_and_waiting():
 
     global_download_status = {
         NODE_A: [
-            DownloadCompleted(
-                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
-            )
+            DownloadCompleted(shard_metadata=shard1, node_id=NODE_A, total=Memory())
         ],
         NODE_B: [
-            DownloadCompleted(
-                shard_metadata=shard2, node_id=NODE_B, total_bytes=Memory()
-            )
+            DownloadCompleted(shard_metadata=shard2, node_id=NODE_B, total=Memory())
         ],
     }
 
@@ -138,9 +134,7 @@ def test_plan_does_not_request_download_when_shard_already_downloaded():
     # Global state shows shard is downloaded for NODE_A
     global_download_status: dict[NodeId, list[DownloadProgress]] = {
         NODE_A: [
-            DownloadCompleted(
-                shard_metadata=shard, node_id=NODE_A, total_bytes=Memory()
-            )
+            DownloadCompleted(shard_metadata=shard, node_id=NODE_A, total=Memory())
         ],
         NODE_B: [],
     }
@@ -187,9 +181,7 @@ def test_plan_does_not_load_model_until_all_shards_downloaded_globally():
 
     global_download_status = {
         NODE_A: [
-            DownloadCompleted(
-                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
-            )
+            DownloadCompleted(shard_metadata=shard1, node_id=NODE_A, total=Memory())
         ],
         NODE_B: [],  # NODE_B has no downloads completed yet
     }
@@ -207,14 +199,10 @@ def test_plan_does_not_load_model_until_all_shards_downloaded_globally():
 
     global_download_status = {
         NODE_A: [
-            DownloadCompleted(
-                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
-            )
+            DownloadCompleted(shard_metadata=shard1, node_id=NODE_A, total=Memory())
         ],
         NODE_B: [
-            DownloadCompleted(
-                shard_metadata=shard2, node_id=NODE_B, total_bytes=Memory()
-            )
+            DownloadCompleted(shard_metadata=shard2, node_id=NODE_B, total=Memory())
         ],  # NODE_B has no downloads completed yet
     }