Fix draft release detection (attempt 3) (#1176)

## Motivation

Previous fix still failed in CI. Suspecting permissions issue with
GITHUB_TOKEN not being able to see draft releases via API.

## Changes

1. Add explicit `permissions: contents: write` to the job
2. Use `gh release list` first to check if draft exists (this uses a
different code path that might work better)
3. Add debug echo statements

## Test Plan

Delete v1.0.63 tag and re-push after merging.

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

.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"
       }
     }
   ],

src/exo/master/placement_utils.py

@@ -49,22 +49,20 @@ def get_smallest_cycles(cycles: list[list[NodeInfo]]) -> list[list[NodeInfo]]:
     return [cycle for cycle in cycles if len(cycle) == min_nodes]
 
 
-def _assign_layers_by_ram(
+def get_shard_assignments_for_pipeline_parallel(
     model_meta: ModelMetadata,
     selected_cycle: list[NodeWithProfile],
-) -> ShardAssignments:
-    """Assign layers proportionally based on available RAM."""
+):
+    cycle_memory = sum(
+        (node.node_profile.memory.ram_available for node in selected_cycle),
+        start=Memory(),
+    )
     total_layers = model_meta.n_layers
     world_size = len(selected_cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
 
-    cycle_memory = sum(
-        (node.node_profile.memory.ram_available for node in selected_cycle),
-        start=Memory(),
-    )
     layers_assigned = 0
-
     for i, node in enumerate(selected_cycle):
         if i == len(selected_cycle) - 1:
             node_layers = total_layers - layers_assigned
@@ -79,6 +77,7 @@ def _assign_layers_by_ram(
             node_layers = max(1, node_layers)
 
         runner_id = RunnerId()
+
         shard = PipelineShardMetadata(
             model_meta=model_meta,
             device_rank=i,
@@ -87,143 +86,18 @@ def _assign_layers_by_ram(
             end_layer=layers_assigned + node_layers,
             n_layers=total_layers,
         )
+
         runner_to_shard[runner_id] = shard
         node_to_runner[node.node_id] = runner_id
         layers_assigned += node_layers
 
-    return ShardAssignments(
+    shard_assignments = ShardAssignments(
         model_id=model_meta.model_id,
         runner_to_shard=runner_to_shard,
         node_to_runner=node_to_runner,
     )
 
-
-def _reserve_base_layers(world_size: int, total_layers: int) -> dict[int, int]:
-    """Reserve 1 layer per node to ensure connectivity."""
-    assignments = {i: 0 for i in range(world_size)}
-    remaining_layers = total_layers
-
-    for i in range(world_size):
-        assignments[i] = 1
-        remaining_layers -= 1
-
-    if remaining_layers < 0:
-        logger.warning(
-            "Fewer layers than nodes! Reducing to 1 layer per node where possible."
-        )
-        assignments = {i: 1 if i < total_layers else 0 for i in range(world_size)}
-        remaining_layers = 0
-
-    return assignments
-
-
-def _distribute_layers_by_bandwidth(
-    selected_cycle: list[NodeWithProfile],
-    assignments: dict[int, int],
-    remaining_layers: int,
-    model_meta: ModelMetadata,
-) -> None:
-    """Distribute remaining layers based on bandwidth and RAM capacity."""
-    indexed_nodes = list(enumerate(selected_cycle))
-    sorted_nodes = sorted(
-        indexed_nodes,
-        key=lambda x: x[1].node_profile.memory_bandwidth or 0,
-        reverse=True,
-    )
-
-    for original_idx, node in sorted_nodes:
-        if remaining_layers <= 0:
-            break
-
-        layer_size_bytes = model_meta.storage_size.in_bytes / model_meta.n_layers
-        max_layers_by_ram = int(
-            node.node_profile.memory.ram_available.in_bytes // layer_size_bytes
-        )
-        can_take = max(0, max_layers_by_ram - assignments[original_idx])
-        take = min(can_take, remaining_layers)
-        assignments[original_idx] += take
-        remaining_layers -= take
-
-    if remaining_layers > 0:
-        logger.warning(
-            "All nodes maxed out on RAM estimation, dumping remaining layers on fastest nodes."
-        )
-        for original_idx, _ in sorted_nodes:
-            assignments[original_idx] += 1
-            remaining_layers -= 1
-            if remaining_layers == 0:
-                break
-
-
-def _create_shard_assignments(
-    model_meta: ModelMetadata,
-    selected_cycle: list[NodeWithProfile],
-    assignments: dict[int, int],
-) -> ShardAssignments:
-    """Create shard assignments from layer assignments."""
-    world_size = len(selected_cycle)
-    runner_to_shard: dict[RunnerId, ShardMetadata] = {}
-    node_to_runner: dict[NodeId, RunnerId] = {}
-
-    current_start = 0
-    for i, node in enumerate(selected_cycle):
-        count = assignments[i]
-        runner_id = RunnerId()
-        shard = PipelineShardMetadata(
-            model_meta=model_meta,
-            device_rank=i,
-            world_size=world_size,
-            start_layer=current_start,
-            end_layer=current_start + count,
-            n_layers=model_meta.n_layers,
-        )
-        runner_to_shard[runner_id] = shard
-        node_to_runner[node.node_id] = runner_id
-        current_start += count
-
-    return ShardAssignments(
-        model_id=model_meta.model_id,
-        runner_to_shard=runner_to_shard,
-        node_to_runner=node_to_runner,
-    )
-
-
-def _assign_layers_by_bandwidth(
-    model_meta: ModelMetadata,
-    selected_cycle: list[NodeWithProfile],
-) -> ShardAssignments:
-    """Assign layers based on memory bandwidth."""
-    logger.info("Using bandwidth-aware shard assignment")
-
-    total_layers = model_meta.n_layers
-    world_size = len(selected_cycle)
-
-    assignments = _reserve_base_layers(world_size, total_layers)
-    remaining_layers = total_layers - sum(assignments.values())
-
-    if remaining_layers > 0:
-        _distribute_layers_by_bandwidth(
-            selected_cycle, assignments, remaining_layers, model_meta
-        )
-
-    return _create_shard_assignments(model_meta, selected_cycle, assignments)
-
-
-def get_shard_assignments_for_pipeline_parallel(
-    model_meta: ModelMetadata,
-    selected_cycle: list[NodeWithProfile],
-):
-    has_bandwidth = all(
-        node.node_profile.memory_bandwidth is not None for node in selected_cycle
-    )
-
-    if not has_bandwidth:
-        logger.info(
-            "Bandwidth data missing for some nodes, falling back to RAM-proportional assignment"
-        )
-        return _assign_layers_by_ram(model_meta, selected_cycle)
-
-    return _assign_layers_by_bandwidth(model_meta, selected_cycle)
+    return shard_assignments
 
 
 def get_shard_assignments_for_tensor_parallel(

src/exo/master/tests/test_placement_utils.py

@@ -397,106 +397,3 @@ def test_get_mlx_jaccl_coordinators(
     assert coordinators[node_c_id] == (
         f"{conn_c_a.send_back_multiaddr.ip_address}:5000"
     ), "node_c should use the IP from conn_c_a"
-
-
-def test_get_shard_assignments_bandwidth_aware(
-    topology: Topology,
-    create_node: Callable[[int, NodeId | None], NodeInfo],
-    create_connection: Callable[[NodeId, NodeId], Connection],
-):
-    # arrange
-    node_a_id = NodeId()
-    node_b_id = NodeId()
-    node_c_id = NodeId()
-
-    # Create nodes with identical RAM (plenty of it)
-    # Using 1GB to ensure no RAM constraints (model is small)
-    node_a = create_node(1024 * 1024 * 1024, node_a_id)
-    node_b = create_node(1024 * 1024 * 1024, node_b_id)
-    node_c = create_node(1024 * 1024 * 1024, node_c_id)
-
-    # Set Bandwidths: A=400 (Fastest), B=200, C=100 (Slowest)
-    assert node_a.node_profile is not None
-    assert node_b.node_profile is not None
-    assert node_c.node_profile is not None
-
-    node_a.node_profile.memory_bandwidth = 400_000_000_000
-    node_b.node_profile.memory_bandwidth = 200_000_000_000
-    node_c.node_profile.memory_bandwidth = 100_000_000_000
-
-    topology.add_node(node_a)
-    topology.add_node(node_b)
-    topology.add_node(node_c)
-
-    topology.add_connection(create_connection(node_a_id, node_b_id))
-    topology.add_connection(create_connection(node_b_id, node_c_id))
-    topology.add_connection(create_connection(node_c_id, node_a_id))
-
-    # Needs full cycle edges for get_cycles/get_shard_assignments if strict?
-    # Actually get_cycles just looks for cycles.
-    # But let's follow the pattern of other tests if they add bidirectional.
-    # checking test_filter_cycles_by_memory, it adds both directions.
-    topology.add_connection(create_connection(node_b_id, node_a_id))
-    topology.add_connection(create_connection(node_c_id, node_b_id))
-    topology.add_connection(create_connection(node_a_id, node_c_id))
-
-    model_meta = ModelMetadata(
-        model_id=ModelId("test-model"),
-        pretty_name="Test Model",
-        n_layers=30,  # 30 layers
-        storage_size=Memory.from_kb(
-            300
-        ),  # 10KB per layer. Nodes have 100MB RAM (100*1024 in create_node usually means KB? other tests use 1000*1024).
-        # create_node arg is likely KB or Bytes.
-        # test_filter_cycles_by_memory: create_node(1000 * 1024, ...) -> Memory.from_bytes(1) passes.
-        # Let's assume create_node takes Bytes or KB consistently.
-        # If I give 100*1024*1024 bytes = 100MB.
-        # Model storage = 300KB.
-        # So capacity is definitely not an issue.
-        hidden_size=1000,
-        supports_tensor=True,
-    )
-
-    cycles = topology.get_cycles()
-    # Depending on how get_cycles works and order of addition, we might get multiple cycles.
-    # filtering by memory usually done in master.
-    # Here we just pick one.
-    selected_cycle = cycles[0]
-
-    # act
-    shard_assignments = get_shard_assignments(
-        model_meta, selected_cycle, Sharding.Pipeline
-    )
-
-    # assert
-    runner_id_a = shard_assignments.node_to_runner[node_a_id]
-    runner_id_b = shard_assignments.node_to_runner[node_b_id]
-    runner_id_c = shard_assignments.node_to_runner[node_c_id]
-
-    # Get layer counts
-    layers_a = (
-        shard_assignments.runner_to_shard[runner_id_a].end_layer
-        - shard_assignments.runner_to_shard[runner_id_a].start_layer
-    )
-    layers_b = (
-        shard_assignments.runner_to_shard[runner_id_b].end_layer
-        - shard_assignments.runner_to_shard[runner_id_b].start_layer
-    )
-    layers_c = (
-        shard_assignments.runner_to_shard[runner_id_c].end_layer
-        - shard_assignments.runner_to_shard[runner_id_c].start_layer
-    )
-
-    # Check total
-    assert layers_a + layers_b + layers_c == 30
-
-    # Check that the fastest node (A with 400GB/s) gets saturated first.
-    # With strict greedy assignment and plenty of RAM:
-    # 1. Reserve: A=1, B=1, C=1. Remaining=27.
-    # 2. Sort: [A, B, C]
-    # 3. A takes min(remaining=27, capacity=huge) = 27.
-    # 4. A=28, B=1, C=1.
-
-    assert layers_a == 28
-    assert layers_b == 1
-    assert layers_c == 1

src/exo/shared/types/profiling.py

@@ -57,7 +57,6 @@ class NodePerformanceProfile(CamelCaseModel):
     chip_id: str
     friendly_name: str
     memory: MemoryPerformanceProfile
-    memory_bandwidth: int | None = None
     network_interfaces: list[NetworkInterfaceInfo] = []
     system: SystemPerformanceProfile
 

src/exo/worker/utils/profile.py

@@ -4,7 +4,6 @@ import platform
 from typing import Any, Callable, Coroutine
 
 import anyio
-from anyio import to_thread
 from loguru import logger
 
 from exo.shared.types.memory import Memory
@@ -25,61 +24,8 @@ from .system_info import (
     get_friendly_name,
     get_model_and_chip,
     get_network_interfaces,
-    profile_memory_bandwidth,
 )
 
-# Module-level cache for memory bandwidth (doesn't change at runtime)
-_cached_bandwidth: int | None = None
-_bandwidth_profiled: bool = False
-_bandwidth_profiling_task: asyncio.Task[int | None] | None = None
-
-
-async def profile_bandwidth_once() -> int | None:
-    """Profile bandwidth once in a background thread and cache the result.
-
-    This function is non-blocking - it runs the profiling in a thread pool.
-    Subsequent calls return the cached result immediately.
-    """
-    global _cached_bandwidth, _bandwidth_profiled, _bandwidth_profiling_task
-
-    # Already profiled, return cached value
-    if _bandwidth_profiled:
-        return _cached_bandwidth
-
-    # Profiling already in progress, wait for it
-    if _bandwidth_profiling_task is not None:
-        return await _bandwidth_profiling_task
-
-    # Start profiling in background thread
-    async def _do_profile() -> int | None:
-        global _cached_bandwidth, _bandwidth_profiled
-        try:
-            logger.info("Starting memory bandwidth profiling in background thread...")
-            bandwidth = await to_thread.run_sync(profile_memory_bandwidth, cancellable=True)
-            _cached_bandwidth = bandwidth
-            _bandwidth_profiled = True
-            if bandwidth:
-                logger.info(f"Memory bandwidth profiled: {bandwidth / 1e9:.1f} GB/s")
-            else:
-                logger.warning("Memory bandwidth profiling returned None")
-            return bandwidth
-        except Exception as e:
-            logger.opt(exception=e).error("Memory bandwidth profiling failed")
-            _bandwidth_profiled = True  # Mark as done to avoid retrying
-            return None
-
-    _bandwidth_profiling_task = asyncio.create_task(_do_profile())
-    return await _bandwidth_profiling_task
-
-
-def get_memory_bandwidth_cached() -> int | None:
-    """Return cached bandwidth or None if not yet profiled.
-
-    This is a non-blocking synchronous function that returns immediately.
-    Call profile_bandwidth_once() first to trigger profiling.
-    """
-    return _cached_bandwidth if _bandwidth_profiled else None
-
 
 async def get_metrics_async() -> Metrics | None:
     """Return detailed Metrics on macOS or a minimal fallback elsewhere."""
@@ -125,8 +71,6 @@ async def start_polling_node_metrics(
     callback: Callable[[NodePerformanceProfile], Coroutine[Any, Any, None]],
 ):
     poll_interval_s = 1.0
-    bandwidth_profile_started = False
-
     while True:
         try:
             metrics = await get_metrics_async()
@@ -141,15 +85,6 @@ async def start_polling_node_metrics(
             # do the memory profile last to get a fresh reading to not conflict with the other memory profiling loop
             memory_profile = get_memory_profile()
 
-            # Start bandwidth profiling in background on first poll (non-blocking)
-            if not bandwidth_profile_started:
-                bandwidth_profile_started = True
-                # Fire and forget - don't await, let it run in background
-                asyncio.create_task(profile_bandwidth_once())
-
-            # Use cached bandwidth (None until profiling completes)
-            memory_bandwidth = get_memory_bandwidth_cached()
-
             await callback(
                 NodePerformanceProfile(
                     model_id=model_id,
@@ -157,7 +92,6 @@ async def start_polling_node_metrics(
                     friendly_name=friendly_name,
                     network_interfaces=network_interfaces,
                     memory=memory_profile,
-                    memory_bandwidth=memory_bandwidth,
                     system=SystemPerformanceProfile(
                         gpu_usage=metrics.gpu_usage[1],
                         temp=metrics.temp.gpu_temp_avg,

src/exo/worker/utils/system_info.py

@@ -1,6 +1,5 @@
 import socket
 import sys
-import time
 from subprocess import CalledProcessError
 
 import psutil
@@ -82,68 +81,3 @@ async def get_model_and_chip() -> tuple[str, str]:
     chip = chip_line.split(": ")[1] if chip_line else "Unknown Chip"
 
     return (model, chip)
-
-
-def profile_memory_bandwidth() -> int | None:
-    """
-    Profile device memory bandwidth using MLX GPU operations.
-
-    Uses a large array copy on the GPU to measure unified memory bandwidth.
-    Returns measured bandwidth in bytes/second, or None if MLX is unavailable.
-    """
-    try:
-        import mlx.core as mx
-
-        if not mx.metal.is_available():
-            return None
-
-        # Use 2GB buffer to better saturate memory bandwidth
-        # Use 2D shape to avoid potential issues with very large 1D arrays
-        size_bytes = 2 * 1024 * 1024 * 1024
-        side = int((size_bytes // 4) ** 0.5)  # Square 2D array of float32
-        shape = (side, side)
-        actual_bytes = side * side * 4
-        bytes_transferred = actual_bytes * 2  # read + write
-
-        # Warm-up: run the full benchmark operation multiple times to stabilize GPU
-        for _ in range(3):
-            src = mx.random.uniform(shape=shape, dtype=mx.float32)
-            mx.eval(src)
-            dst = src + 0.0
-            mx.eval(dst)
-            mx.synchronize()
-            del src, dst
-
-        # Benchmark: measure time to copy array
-        best_bandwidth = 0.0
-        num_runs = 4
-
-        for _ in range(num_runs):
-            src = mx.random.uniform(shape=shape, dtype=mx.float32)
-            mx.eval(src)
-            mx.synchronize()
-
-            # Time the copy operation (src + 0.0 forces read of src, write of dst)
-            start = time.perf_counter()
-            dst = src + 0.0
-            mx.eval(dst)
-            mx.synchronize()
-            end = time.perf_counter()
-
-            bandwidth = bytes_transferred / (end - start)
-            best_bandwidth = max(best_bandwidth, bandwidth)
-
-            del src, dst
-
-        return int(best_bandwidth)
-    except Exception:
-        return None
-
-
-def get_memory_bandwidth(_chip_id: str) -> int | None:
-    """
-    Returns measured memory bandwidth in bytes/second.
-
-    Uses MLX GPU operations for accurate unified memory bandwidth measurement.
-    """
-    return profile_memory_bandwidth()