Handle model timeouts (#1177)

- Add eval with a timeout.
- Add fast synch flag

## Motivation

Because of the experimental FAST SYNCH flag, some models may not work.
This PR catches when this occurs and allows users to specify a run
without fast synch

## Changes

- Adds a flag to enable or disable fast synch (--fast-synch and
--no-fast-synch)
- Adds a heuristic timeout
- Reduces exo_bench default timeout to 10 minutes.

## Why It Works

Heuristic timeout assumes normal loading times on Mac devices (60 +
model size in gb / 5: e.g. DeepSeek takes up to 120 seconds to load on
tensor parallel, and timeout is set to 60 + 120 = 180s.

We could raise this value if necessary.

## Test Plan

### Manual Testing
Catches that GPT OSS fails to load in Tensor RDMA
Can launch with --no-fast-synch flag to launch GPT OSS.

**GPT OSS 20B**
TP with fast synch
<img width="3064" height="456" alt="image"
src="https://github.com/user-attachments/assets/f6e25cd8-8621-4e99-99fe-292ee05c4035"
/>

TP without fast synch
<img width="3098" height="496" alt="image"
src="https://github.com/user-attachments/assets/d36453d9-6686-4cfe-aa7c-a7d458369d4d"
/>
[Note: the performance is really not great as fast synch is off]

(As a sanity check)
PP with fast synch
<img width="3124" height="496" alt="image"
src="https://github.com/user-attachments/assets/e97d4547-c6fa-483d-badb-4b371b900b4c"
/>

PP without fast synch
<img width="3078" height="508" alt="image"
src="https://github.com/user-attachments/assets/b2e20dfd-4b0e-4295-8a92-417dfe745c28"
/>

PP without RDMA
<img width="3070" height="498" alt="image"
src="https://github.com/user-attachments/assets/a8509d68-0aef-4cda-bca5-a67d39a0801e"
/>

TP without RDMA
<img width="3068" height="496" alt="image"
src="https://github.com/user-attachments/assets/b5691429-89f4-4369-bcf2-8fde2ad7154a"
/>

.github/workflows/build-app.yml

@@ -1,5 +1,16 @@
 name: Build EXO macOS DMG
 
+# Release workflow:
+# 1. Create a draft GitHub Release with the tag name (e.g. v1.0.0) and write release notes in markdown
+# 2. Push the tag: git tag v1.0.0 && git push origin v1.0.0
+# 3. This workflow builds, signs, and notarizes the DMG
+# 4. Release notes are embedded in appcast.xml for Sparkle (rendered as markdown)
+# 5. DMG and appcast.xml are uploaded to S3
+# 6. The draft GitHub Release is published with the DMG attached
+#
+# For alpha releases (e.g. v1.0.0-alpha.1): draft release and notes are optional.
+# If no draft exists, a release is auto-created with generated notes.
+
 on:
   workflow_dispatch:
   push:
@@ -11,8 +22,10 @@ on:
 jobs:
   build-macos-app:
     runs-on: "macos-26"
+    permissions:
+      contents: write
     env:
-      SPARKLE_VERSION: 2.8.1
+      SPARKLE_VERSION: 2.9.0-beta.1
       SPARKLE_DOWNLOAD_PREFIX: ${{ secrets.SPARKLE_DOWNLOAD_PREFIX }}
       SPARKLE_FEED_URL: ${{ secrets.SPARKLE_FEED_URL }}
       SPARKLE_ED25519_PUBLIC: ${{ secrets.SPARKLE_ED25519_PUBLIC }}
@@ -87,6 +100,52 @@ jobs:
             exit 1
           fi
 
+      - name: Fetch and validate release notes
+        if: github.ref_type == 'tag'
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          # Find draft release by name using gh release list (more reliable with default token)
+          echo "Looking for draft release named '$GITHUB_REF_NAME'..."
+          DRAFT_EXISTS=$(gh release list --json name,isDraft --jq ".[] | select(.isDraft == true) | select(.name == \"$GITHUB_REF_NAME\") | .name" 2>/dev/null || echo "")
+
+          if [[ -z "$DRAFT_EXISTS" ]]; then
+            if [[ "$IS_ALPHA" == "true" ]]; then
+              echo "No draft release found for alpha tag $GITHUB_REF_NAME (optional for alphas)"
+              echo "HAS_RELEASE_NOTES=false" >> $GITHUB_ENV
+              exit 0
+            fi
+            echo "ERROR: No draft release found for tag $GITHUB_REF_NAME"
+            echo "Please create a draft release with release notes before pushing the tag."
+            exit 1
+          fi
+
+          # Fetch full release details via API to get body and ID
+          echo "Found draft release, fetching details..."
+          RELEASE_JSON=$(gh api repos/${{ github.repository }}/releases --jq ".[] | select(.draft == true) | select(.name == \"$GITHUB_REF_NAME\")" 2>/dev/null || echo "")
+
+          # Extract release notes
+          NOTES=$(echo "$RELEASE_JSON" | jq -r '.body // ""')
+          if [[ -z "$NOTES" || "$NOTES" == "null" ]]; then
+            if [[ "$IS_ALPHA" == "true" ]]; then
+              echo "Draft release has no notes (optional for alphas)"
+              echo "HAS_RELEASE_NOTES=false" >> $GITHUB_ENV
+              exit 0
+            fi
+            echo "ERROR: Draft release exists but has no release notes"
+            echo "Please add release notes to the draft release before pushing the tag."
+            exit 1
+          fi
+
+          # Save release ID for later publishing
+          RELEASE_ID=$(echo "$RELEASE_JSON" | jq -r '.id')
+          echo "DRAFT_RELEASE_ID=$RELEASE_ID" >> $GITHUB_ENV
+          echo "HAS_RELEASE_NOTES=true" >> $GITHUB_ENV
+
+          echo "Found draft release (ID: $RELEASE_ID), saving release notes..."
+          echo "$NOTES" > /tmp/release_notes.md
+          echo "RELEASE_NOTES_FILE=/tmp/release_notes.md" >> $GITHUB_ENV
+
       # ============================================================
       # Install dependencies
       # ============================================================
@@ -304,6 +363,28 @@ jobs:
             $CHANNEL_FLAG \
             .
 
+      - name: Inject release notes into appcast
+        if: github.ref_type == 'tag' && env.HAS_RELEASE_NOTES == 'true'
+        env:
+          RELEASE_VERSION: ${{ env.RELEASE_VERSION }}
+        run: |
+          # Inject markdown release notes with sparkle:format="markdown" (Sparkle 2.9+)
+          export NOTES=$(cat "$RELEASE_NOTES_FILE")
+
+          # Insert description after the enclosure tag for this version
+          awk '
+            /<enclosure[^>]*>/ && index($0, ENVIRON["RELEASE_VERSION"]) {
+              print
+              print "            <description sparkle:format=\"markdown\"><![CDATA["
+              print ENVIRON["NOTES"]
+              print "            ]]></description>"
+              next
+            }
+            { print }
+          ' output/appcast.xml > output/appcast.xml.tmp && mv output/appcast.xml.tmp output/appcast.xml
+
+          echo "Injected markdown release notes for version $RELEASE_VERSION"
+
       # ============================================================
       # Upload artifacts
       # ============================================================
@@ -336,3 +417,26 @@ jobs:
             aws s3 cp "$DMG_NAME" "s3://${SPARKLE_S3_BUCKET}/${PREFIX}EXO-latest.dmg"
             aws s3 cp appcast.xml "s3://${SPARKLE_S3_BUCKET}/${PREFIX}appcast.xml" --content-type application/xml --cache-control no-cache
           fi
+
+      - name: Publish GitHub Release
+        if: github.ref_type == 'tag'
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          DMG_PATH="output/EXO-${RELEASE_VERSION}.dmg"
+
+          if [[ "$HAS_RELEASE_NOTES" == "true" ]]; then
+            # Update the draft release with the tag and upload DMG
+            gh api --method PATCH "repos/${{ github.repository }}/releases/$DRAFT_RELEASE_ID" \
+              -f tag_name="$GITHUB_REF_NAME" \
+              -F draft=false
+            gh release upload "$GITHUB_REF_NAME" "$DMG_PATH" --clobber
+            echo "Published release $GITHUB_REF_NAME with DMG attached"
+          else
+            # Alpha without draft release - create one with auto-generated notes
+            gh release create "$GITHUB_REF_NAME" "$DMG_PATH" \
+              --title "$GITHUB_REF_NAME" \
+              --generate-notes \
+              --prerelease
+            echo "Created alpha release $GITHUB_REF_NAME with auto-generated notes"
+          fi

app/EXO/EXO.xcodeproj/project.pbxproj

@@ -585,7 +585,7 @@
 			repositoryURL = "https://github.com/sparkle-project/Sparkle.git";
 			requirement = {
 				kind = upToNextMajorVersion;
-				minimumVersion = 2.8.1;
+				minimumVersion = 2.9.0-beta.1;
 			};
 		};
 /* End XCRemoteSwiftPackageReference section */

app/EXO/EXO.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved

@@ -6,8 +6,8 @@
       "kind" : "remoteSourceControl",
       "location" : "https://github.com/sparkle-project/Sparkle.git",
       "state" : {
-        "revision" : "5581748cef2bae787496fe6d61139aebe0a451f6",
-        "version" : "2.8.1"
+        "revision" : "e641adb41915a8409895e2e30666aa64e487b637",
+        "version" : "2.9.0-beta.1"
       }
     }
   ],

bench/exo_bench.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import argparse
+import contextlib
 import http.client
 import json
 import os
@@ -26,7 +27,7 @@ class ExoHttpError(RuntimeError):
 
 
 class ExoClient:
-    def __init__(self, host: str, port: int, timeout_s: float = 2400.0):
+    def __init__(self, host: str, port: int, timeout_s: float = 600.0):
         self.host = host
         self.port = port
         self.timeout_s = timeout_s
@@ -104,22 +105,46 @@ def runner_ready(runner: dict[str, Any]) -> bool:
     return "RunnerReady" in runner
 
 
+def runner_failed(runner: dict[str, Any]) -> bool:
+    return "RunnerFailed" in runner
+
+
+def get_runner_failed_message(runner: dict[str, Any]) -> str | None:
+    if "RunnerFailed" in runner:
+        return runner["RunnerFailed"].get("errorMessage")
+    return None
+
+
 def wait_for_instance_ready(
     client: ExoClient, instance_id: str, timeout: float = 24000.0
 ) -> None:
     start_time = time.time()
+    instance_existed = False
     while time.time() - start_time < timeout:
         state = client.request_json("GET", "/state")
         instances = state.get("instances", {})
 
         if instance_id not in instances:
+            if instance_existed:
+                # Instance was deleted after being created - likely due to runner failure
+                raise RuntimeError(
+                    f"Instance {instance_id} was deleted (runner may have failed)"
+                )
             time.sleep(0.1)
             continue
 
+        instance_existed = True
         instance = instances[instance_id]
         runner_ids = runner_ids_from_instance(instance)
         runners = state.get("runners", {})
 
+        # Check for failed runners first
+        for rid in runner_ids:
+            runner = runners.get(rid, {})
+            if runner_failed(runner):
+                error_msg = get_runner_failed_message(runner) or "Unknown error"
+                raise RuntimeError(f"Runner {rid} failed: {error_msg}")
+
         if all(runner_ready(runners.get(rid, {})) for rid in runner_ids):
             return
 
@@ -299,6 +324,12 @@ def main() -> int:
         default=4,
         help="Only consider placements using <= this many nodes.",
     )
+    ap.add_argument(
+        "--min-nodes",
+        type=int,
+        default=1,
+        help="Only consider placements using >= this many nodes.",
+    )
     ap.add_argument(
         "--instance-meta", choices=["ring", "jaccl", "both"], default="both"
     )
@@ -320,7 +351,7 @@ def main() -> int:
         help="Warmup runs per placement (uses first pp/tg).",
     )
     ap.add_argument(
-        "--timeout", type=float, default=2400.0, help="HTTP timeout (seconds)."
+        "--timeout", type=float, default=600.0, help="HTTP timeout (seconds)."
     )
     ap.add_argument(
         "--json-out",
@@ -399,7 +430,7 @@ def main() -> int:
         ):
             continue
 
-        if 0 < n <= args.max_nodes:
+        if args.min_nodes <= n <= args.max_nodes:
             selected.append(p)
 
     if not selected:
@@ -441,7 +472,13 @@ def main() -> int:
         )
 
         client.request_json("POST", "/instance", body={"instance": instance})
-        wait_for_instance_ready(client, instance_id)
+        try:
+            wait_for_instance_ready(client, instance_id)
+        except (RuntimeError, TimeoutError) as e:
+            logger.error(f"Failed to initialize placement: {e}")
+            with contextlib.suppress(ExoHttpError):
+                client.request_json("DELETE", f"/instance/{instance_id}")
+            continue
 
         time.sleep(1)
 

src/exo/main.py

@@ -205,6 +205,14 @@ def main():
     logger.info("Starting EXO")
     logger.info(f"EXO_LIBP2P_NAMESPACE: {os.getenv('EXO_LIBP2P_NAMESPACE')}")
 
+    # Set FAST_SYNCH override env var for runner subprocesses
+    if args.fast_synch is True:
+        os.environ["EXO_FAST_SYNCH"] = "on"
+        logger.info("FAST_SYNCH forced ON")
+    elif args.fast_synch is False:
+        os.environ["EXO_FAST_SYNCH"] = "off"
+        logger.info("FAST_SYNCH forced OFF")
+
     node = anyio.run(Node.create, args)
     anyio.run(node.run)
     logger.info("EXO Shutdown complete")
@@ -218,6 +226,7 @@ class Args(CamelCaseModel):
     api_port: PositiveInt = 52415
     tb_only: bool = False
     no_worker: bool = False
+    fast_synch: bool | None = None  # None = auto, True = force on, False = force off
 
     @classmethod
     def parse(cls) -> Self:
@@ -259,6 +268,20 @@ class Args(CamelCaseModel):
             "--no-worker",
             action="store_true",
         )
+        fast_synch_group = parser.add_mutually_exclusive_group()
+        fast_synch_group.add_argument(
+            "--fast-synch",
+            action="store_true",
+            dest="fast_synch",
+            default=None,
+            help="Force MLX FAST_SYNCH on (for JACCL backend)",
+        )
+        fast_synch_group.add_argument(
+            "--no-fast-synch",
+            action="store_false",
+            dest="fast_synch",
+            help="Force MLX FAST_SYNCH off",
+        )
 
         args = parser.parse_args()
         return cls(**vars(args))  # pyright: ignore[reportAny] - We are intentionally validating here, we can't do it statically

src/exo/master/api.py

@@ -1,13 +1,14 @@
 import time
 from collections.abc import AsyncGenerator
+from http import HTTPStatus
 from typing import cast
 
 import anyio
-from anyio import create_task_group
+from anyio import BrokenResourceError, create_task_group
 from anyio.abc import TaskGroup
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 from fastapi.staticfiles import StaticFiles
 from hypercorn.asyncio import serve  # pyright: ignore[reportUnknownVariableType]
 from hypercorn.config import Config
@@ -29,6 +30,8 @@ from exo.shared.types.api import (
     CreateInstanceParams,
     CreateInstanceResponse,
     DeleteInstanceResponse,
+    ErrorInfo,
+    ErrorResponse,
     FinishReason,
     GenerationStats,
     ModelList,
@@ -49,7 +52,12 @@ from exo.shared.types.commands import (
     TaskFinished,
 )
 from exo.shared.types.common import CommandId, NodeId, SessionId
-from exo.shared.types.events import ChunkGenerated, Event, ForwarderEvent, IndexedEvent
+from exo.shared.types.events import (
+    ChunkGenerated,
+    Event,
+    ForwarderEvent,
+    IndexedEvent,
+)
 from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
 from exo.shared.types.state import State
@@ -115,6 +123,7 @@ class API:
         self.paused_ev: anyio.Event = anyio.Event()
 
         self.app = FastAPI()
+        self._setup_exception_handlers()
         self._setup_cors()
         self._setup_routes()
 
@@ -145,6 +154,20 @@ class API:
         self.paused_ev.set()
         self.paused_ev = anyio.Event()
 
+    def _setup_exception_handlers(self) -> None:
+        @self.app.exception_handler(HTTPException)
+        async def http_exception_handler(  # pyright: ignore[reportUnusedFunction]
+            _: Request, exc: HTTPException
+        ) -> JSONResponse:
+            err = ErrorResponse(
+                error=ErrorInfo(
+                    message=exc.detail,
+                    type=HTTPStatus(exc.status_code).phrase,
+                    code=exc.status_code,
+                )
+            )
+            return JSONResponse(err.model_dump(), status_code=exc.status_code)
+
     def _setup_cors(self) -> None:
         self.app.add_middleware(
             CORSMiddleware,
@@ -406,6 +429,18 @@ class API:
         """Generate chat completion stream as JSON strings."""
 
         async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                error_response = ErrorResponse(
+                    error=ErrorInfo(
+                        message=chunk.error_message or "Internal server error",
+                        type="InternalServerError",
+                        code=500,
+                    )
+                )
+                yield f"data: {error_response.model_dump_json()}\n\n"
+                yield "data: [DONE]\n\n"
+                return
+
             chunk_response: ChatCompletionResponse = chunk_to_response(
                 chunk, command_id
             )
@@ -426,6 +461,12 @@ class API:
         finish_reason: FinishReason | None = None
 
         async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                raise HTTPException(
+                    status_code=500,
+                    detail=chunk.error_message or "Internal server error",
+                )
+
             if model is None:
                 model = chunk.model
 
@@ -463,6 +504,12 @@ class API:
         stats: GenerationStats | None = None
 
         async for chunk in self._chat_chunk_stream(command_id):
+            if chunk.finish_reason == "error":
+                raise HTTPException(
+                    status_code=500,
+                    detail=chunk.error_message or "Internal server error",
+                )
+
             if model is None:
                 model = chunk.model
 
@@ -607,14 +654,14 @@ class API:
                 for idx, event in self.event_buffer.drain_indexed():
                     self._event_log.append(event)
                     self.state = apply(self.state, IndexedEvent(event=event, idx=idx))
-                    if (
-                        isinstance(event, ChunkGenerated)
-                        and event.command_id in self._chat_completion_queues
-                    ):
+                    if isinstance(event, ChunkGenerated):
                         assert isinstance(event.chunk, TokenChunk)
-                        await self._chat_completion_queues[event.command_id].send(
-                            event.chunk
-                        )
+                        queue = self._chat_completion_queues.get(event.command_id)
+                        if queue is not None:
+                            try:
+                                await queue.send(event.chunk)
+                            except BrokenResourceError:
+                                self._chat_completion_queues.pop(event.command_id, None)
 
     async def _pause_on_new_election(self):
         with self.election_receiver as ems:

src/exo/master/tests/test_api_error_handling.py

@@ -0,0 +1,107 @@
+# pyright: reportUnusedFunction=false, reportAny=false
+from typing import Any, get_args
+
+from fastapi import FastAPI, HTTPException
+from fastapi.testclient import TestClient
+
+from exo.shared.types.api import ErrorInfo, ErrorResponse, FinishReason
+from exo.shared.types.chunks import TokenChunk
+from exo.worker.tests.constants import MODEL_A_ID
+
+
+def test_http_exception_handler_formats_openai_style() -> None:
+    """Test that HTTPException is converted to OpenAI-style error format."""
+    from exo.master.api import API
+
+    app = FastAPI()
+
+    # Setup exception handler
+    api = object.__new__(API)
+    api.app = app
+    api._setup_exception_handlers()  # pyright: ignore[reportPrivateUsage]
+
+    # Add test routes that raise HTTPException
+    @app.get("/test-error")
+    async def _test_error() -> None:
+        raise HTTPException(status_code=500, detail="Test error message")
+
+    @app.get("/test-not-found")
+    async def _test_not_found() -> None:
+        raise HTTPException(status_code=404, detail="Resource not found")
+
+    client = TestClient(app)
+
+    # Test 500 error
+    response = client.get("/test-error")
+    assert response.status_code == 500
+    data: dict[str, Any] = response.json()
+    assert "error" in data
+    assert data["error"]["message"] == "Test error message"
+    assert data["error"]["type"] == "Internal Server Error"
+    assert data["error"]["code"] == 500
+
+    # Test 404 error
+    response = client.get("/test-not-found")
+    assert response.status_code == 404
+    data = response.json()
+    assert "error" in data
+    assert data["error"]["message"] == "Resource not found"
+    assert data["error"]["type"] == "Not Found"
+    assert data["error"]["code"] == 404
+
+
+def test_finish_reason_includes_error() -> None:
+    valid_reasons = get_args(FinishReason)
+    assert "error" in valid_reasons
+
+
+def test_token_chunk_with_error_fields() -> None:
+    chunk = TokenChunk(
+        idx=0,
+        model=MODEL_A_ID,
+        text="",
+        token_id=0,
+        finish_reason="error",
+        error_message="Something went wrong",
+    )
+
+    assert chunk.finish_reason == "error"
+    assert chunk.error_message == "Something went wrong"
+
+
+def test_token_chunk_without_error() -> None:
+    chunk = TokenChunk(
+        idx=1,
+        model=MODEL_A_ID,
+        text="Hello",
+        token_id=42,
+        finish_reason=None,
+    )
+
+    assert chunk.finish_reason is None
+    assert chunk.error_message is None
+
+
+def test_error_response_construction() -> None:
+    error_response = ErrorResponse(
+        error=ErrorInfo(
+            message="Generation failed",
+            type="InternalServerError",
+            code=500,
+        )
+    )
+
+    assert error_response.error.message == "Generation failed"
+    assert error_response.error.code == 500
+
+
+def test_normal_finish_reasons_still_work() -> None:
+    for reason in ["stop", "length", "tool_calls", "content_filter", "function_call"]:
+        chunk = TokenChunk(
+            idx=0,
+            model=MODEL_A_ID,
+            text="done",
+            token_id=100,
+            finish_reason=reason,  # type: ignore[arg-type]
+        )
+        assert chunk.finish_reason == reason

src/exo/shared/types/api.py

@@ -11,10 +11,21 @@ from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 
 FinishReason = Literal[
-    "stop", "length", "tool_calls", "content_filter", "function_call"
+    "stop", "length", "tool_calls", "content_filter", "function_call", "error"
 ]
 
 
+class ErrorInfo(BaseModel):
+    message: str
+    type: str
+    param: str | None = None
+    code: int
+
+
+class ErrorResponse(BaseModel):
+    error: ErrorInfo
+
+
 class ModelListModel(BaseModel):
     id: str
     object: str = "model"

src/exo/shared/types/chunks.py

@@ -22,6 +22,7 @@ class TokenChunk(BaseChunk):
     token_id: int
     finish_reason: FinishReason | None = None
     stats: GenerationStats | None = None
+    error_message: str | None = None
 
 
 class ImageChunk(BaseChunk):

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

@@ -2,7 +2,9 @@ import json
 import os
 import resource
 import sys
+import threading
 import time
+from collections.abc import Callable
 from pathlib import Path
 from typing import Any, cast
 
@@ -82,6 +84,45 @@ def get_weights_size(model_shard_meta: ShardMetadata) -> Memory:
     )
 
 
+class ModelLoadingTimeoutError(Exception):
+    pass
+
+
+TimeoutCallback = Callable[[], None]
+
+
+def eval_with_timeout(
+    mlx_item: Any,  # pyright: ignore[reportAny]
+    timeout_seconds: float = 60.0,
+    on_timeout: TimeoutCallback | None = None,
+) -> None:
+    """Evaluate MLX item with a hard timeout.
+
+    If on_timeout callback is provided, it will be called before terminating
+    the process. This allows the runner to send a failure event before exit.
+    """
+    completed = threading.Event()
+
+    def watchdog() -> None:
+        if not completed.wait(timeout=timeout_seconds):
+            logger.error(
+                f"mlx_item evaluation timed out after {timeout_seconds:.0f}s. "
+                "This may indicate an issue with FAST_SYNCH and tensor parallel sharding. "
+                "Terminating process."
+            )
+            if on_timeout is not None:
+                on_timeout()
+            os._exit(1)
+
+    watchdog_thread = threading.Thread(target=watchdog, daemon=True)
+    watchdog_thread.start()
+
+    try:
+        mx.eval(mlx_item)  # pyright: ignore[reportAny]
+    finally:
+        completed.set()
+
+
 def mx_barrier(group: Group | None = None):
     mx.eval(
         mx.distributed.all_sum(
@@ -188,7 +229,9 @@ def initialize_mlx(
 
 
 def load_mlx_items(
-    bound_instance: BoundInstance, group: Group | None
+    bound_instance: BoundInstance,
+    group: Group | None,
+    on_timeout: TimeoutCallback | None = None,
 ) -> tuple[Model, TokenizerWrapper]:
     if group is None:
         logger.info(f"Single device used for {bound_instance.instance}")
@@ -202,7 +245,9 @@ def load_mlx_items(
     else:
         logger.info("Starting distributed init")
         start_time = time.perf_counter()
-        model, tokenizer = shard_and_load(bound_instance.bound_shard, group=group)
+        model, tokenizer = shard_and_load(
+            bound_instance.bound_shard, group=group, on_timeout=on_timeout
+        )
         end_time = time.perf_counter()
         logger.info(
             f"Time taken to shard and load model: {(end_time - start_time):.2f}s"
@@ -216,6 +261,7 @@ def load_mlx_items(
 def shard_and_load(
     shard_metadata: ShardMetadata,
     group: Group,
+    on_timeout: TimeoutCallback | None = None,
 ) -> tuple[nn.Module, TokenizerWrapper]:
     model_path = build_model_path(shard_metadata.model_meta.model_id)
 
@@ -252,7 +298,15 @@ def shard_and_load(
             logger.info(f"loading model from {model_path} with pipeline parallelism")
             model = pipeline_auto_parallel(model, group, shard_metadata)
 
-    mx.eval(model.parameters())
+    # Estimate timeout based on model size
+    base_timeout = float(os.environ.get("EXO_MODEL_LOAD_TIMEOUT", "60"))
+    model_size_gb = get_weights_size(shard_metadata).in_bytes / (1024**3)
+    timeout_seconds = base_timeout + model_size_gb / 5
+    logger.info(
+        f"Evaluating model parameters with timeout of {timeout_seconds:.0f}s "
+        f"(model size: {model_size_gb:.1f}GB)"
+    )
+    eval_with_timeout(model.parameters(), timeout_seconds, on_timeout)
 
     # TODO: Do we need this?
     mx.eval(model)

src/exo/worker/runner/bootstrap.py

@@ -17,15 +17,23 @@ def entrypoint(
     task_receiver: MpReceiver[Task],
     _logger: "loguru.Logger",
 ) -> None:
-    if (
-        isinstance(bound_instance.instance, MlxJacclInstance)
-        and len(bound_instance.instance.ibv_devices) >= 2
+    fast_synch_override = os.environ.get("EXO_FAST_SYNCH")
+    if fast_synch_override == "on" or (
+        fast_synch_override != "off"
+        and (
+            isinstance(bound_instance.instance, MlxJacclInstance)
+            and len(bound_instance.instance.ibv_devices) >= 2
+        )
     ):
         os.environ["MLX_METAL_FAST_SYNCH"] = "1"
+    else:
+        os.environ["MLX_METAL_FAST_SYNCH"] = "0"
 
     global logger
     logger = _logger
 
+    logger.info(f"Fast synch flag: {os.environ['MLX_METAL_FAST_SYNCH']}")
+
     # Import main after setting global logger - this lets us just import logger from this module
     try:
         from exo.worker.runner.runner import main

src/exo/worker/runner/runner.py

@@ -1,6 +1,8 @@
 import time
 from collections.abc import Generator
+from contextlib import contextmanager
 from functools import cache
+from typing import cast
 
 import mlx.core as mx
 from mlx_lm.models.gpt_oss import Model as GptOssModel
@@ -13,6 +15,7 @@ from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
 
 from exo.shared.types.api import ChatCompletionMessageText
 from exo.shared.types.chunks import TokenChunk
+from exo.shared.types.common import CommandId
 from exo.shared.types.events import (
     ChunkGenerated,
     Event,
@@ -20,6 +23,7 @@ from exo.shared.types.events import (
     TaskAcknowledged,
     TaskStatusUpdated,
 )
+from exo.shared.types.models import ModelId
 from exo.shared.types.tasks import (
     ChatCompletion,
     ConnectToGroup,
@@ -48,6 +52,7 @@ from exo.shared.types.worker.runners import (
     RunnerWarmingUp,
 )
 from exo.utils.channels import MpReceiver, MpSender
+from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.generator.generate import mlx_generate, warmup_inference
 from exo.worker.engines.mlx.utils_mlx import (
     initialize_mlx,
@@ -57,6 +62,33 @@ from exo.worker.engines.mlx.utils_mlx import (
 from exo.worker.runner.bootstrap import logger
 
 
+@contextmanager
+def send_error_chunk_on_exception(
+    event_sender: MpSender[Event],
+    command_id: CommandId,
+    model_id: ModelId,
+    device_rank: int,
+):
+    try:
+        yield
+    except Exception as e:
+        logger.error(e)
+        if device_rank == 0:
+            event_sender.send(
+                ChunkGenerated(
+                    command_id=command_id,
+                    chunk=TokenChunk(
+                        idx=0,
+                        model=model_id,
+                        text="",
+                        token_id=0,
+                        finish_reason="error",
+                        error_message=str(e),
+                    ),
+                )
+            )
+
+
 def main(
     bound_instance: BoundInstance,
     event_sender: MpSender[Event],
@@ -118,7 +150,20 @@ def main(
                         )
                     )
 
-                    model, tokenizer = load_mlx_items(bound_instance, group)
+                    def on_model_load_timeout() -> None:
+                        event_sender.send(
+                            RunnerStatusUpdated(
+                                runner_id=runner_id,
+                                runner_status=RunnerFailed(
+                                    error_message="Model loading timed out"
+                                ),
+                            )
+                        )
+                        time.sleep(0.5)
+
+                    model, tokenizer = load_mlx_items(
+                        bound_instance, group, on_timeout=on_model_load_timeout
+                    )
 
                     current_status = RunnerLoaded()
                     logger.info("runner loaded")
@@ -135,7 +180,7 @@ def main(
 
                     logger.info(f"warming up inference for instance: {instance}")
                     toks = warmup_inference(
-                        model=model,
+                        model=cast(Model, model),
                         tokenizer=tokenizer,
                         # kv_prefix_cache=kv_prefix_cache,  # supply for warmup-time prefix caching
                     )
@@ -148,8 +193,6 @@ def main(
                 case ChatCompletion(task_params=task_params, command_id=command_id) if (
                     isinstance(current_status, RunnerReady)
                 ):
-                    assert model
-                    assert tokenizer
                     logger.info(f"received chat request: {str(task)[:500]}")
                     current_status = RunnerRunning()
                     logger.info("runner running")
@@ -158,41 +201,47 @@ def main(
                             runner_id=runner_id, runner_status=current_status
                         )
                     )
-                    assert task_params.messages[0].content is not None
-                    _check_for_debug_prompts(task_params.messages[0].content)
+                    with send_error_chunk_on_exception(
+                        event_sender,
+                        command_id,
+                        shard_metadata.model_meta.model_id,
+                        shard_metadata.device_rank,
+                    ):
+                        assert model
+                        assert tokenizer
+                        assert task_params.messages[0].content is not None
+                        _check_for_debug_prompts(task_params.messages[0].content)
 
-                    # Generate responses using the actual MLX generation
-                    mlx_generator = mlx_generate(
-                        model=model,
-                        tokenizer=tokenizer,
-                        task=task_params,
-                    )
+                        # Generate responses using the actual MLX generation
+                        mlx_generator = mlx_generate(
+                            model=cast(Model, model),
+                            tokenizer=tokenizer,
+                            task=task_params,
+                        )
 
-                    # GPT-OSS specific parsing to match other model formats.
-                    if isinstance(model, GptOssModel):
-                        mlx_generator = parse_gpt_oss(mlx_generator)
+                        # GPT-OSS specific parsing to match other model formats.
+                        if isinstance(model, GptOssModel):
+                            mlx_generator = parse_gpt_oss(mlx_generator)
 
-                    # TODO: Add tool call parser here
+                        # TODO: Add tool call parser here
 
-                    for response in mlx_generator:
-                        match response:
-                            case GenerationResponse():
-                                if shard_metadata.device_rank == 0:
-                                    event_sender.send(
-                                        ChunkGenerated(
-                                            command_id=command_id,
-                                            chunk=TokenChunk(
-                                                idx=response.token,
-                                                model=shard_metadata.model_meta.model_id,
-                                                text=response.text,
-                                                token_id=response.token,
-                                                finish_reason=response.finish_reason,
-                                                stats=response.stats,
-                                            ),
+                        for response in mlx_generator:
+                            match response:
+                                case GenerationResponse():
+                                    if shard_metadata.device_rank == 0:
+                                        event_sender.send(
+                                            ChunkGenerated(
+                                                command_id=command_id,
+                                                chunk=TokenChunk(
+                                                    idx=response.token,
+                                                    model=shard_metadata.model_meta.model_id,
+                                                    text=response.text,
+                                                    token_id=response.token,
+                                                    finish_reason=response.finish_reason,
+                                                    stats=response.stats,
+                                                ),
+                                            )
                                         )
-                                    )
-                                # case TokenizedResponse():
-                                # TODO: something here ig
 
                     current_status = RunnerReady()
                     logger.info("runner ready")

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

@@ -0,0 +1,50 @@
+# pyright: reportAny=false
+from unittest.mock import MagicMock
+
+from exo.shared.types.chunks import TokenChunk
+from exo.shared.types.common import CommandId
+from exo.shared.types.events import ChunkGenerated
+from exo.worker.runner.runner import send_error_chunk_on_exception
+from exo.worker.tests.constants import MODEL_A_ID
+
+
+def test_send_error_chunk_on_exception_no_error() -> None:
+    event_sender = MagicMock()
+    command_id = CommandId()
+
+    with send_error_chunk_on_exception(
+        event_sender, command_id, MODEL_A_ID, device_rank=0
+    ):
+        _ = 1 + 1
+
+    event_sender.send.assert_not_called()
+
+
+def test_send_error_chunk_on_exception_catches_error() -> None:
+    event_sender = MagicMock()
+    command_id = CommandId()
+
+    with send_error_chunk_on_exception(
+        event_sender, command_id, MODEL_A_ID, device_rank=0
+    ):
+        raise ValueError("test error")
+
+    event_sender.send.assert_called_once()
+    call_args = event_sender.send.call_args[0][0]
+    assert isinstance(call_args, ChunkGenerated)
+    assert call_args.command_id == command_id
+    assert isinstance(call_args.chunk, TokenChunk)
+    assert call_args.chunk.finish_reason == "error"
+    assert call_args.chunk.error_message == "test error"
+
+
+def test_send_error_chunk_on_exception_skips_non_rank_zero() -> None:
+    event_sender = MagicMock()
+    command_id = CommandId()
+
+    with send_error_chunk_on_exception(
+        event_sender, command_id, MODEL_A_ID, device_rank=1
+    ):
+        raise ValueError("test error")
+
+    event_sender.send.assert_not_called()