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[Native] Launch the Muse Glimmer server on the native backend - #24957

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offgraph-kv-mg-native-executorfrom
offgraph-kv-mg-native-serve

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@metascroy

@metascroy metascroy commented Nov 12, 2026 •

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Off-graph KV cache for the native ggml engine, through serving Muse Glimmer on it. Review bottom-up; each PR builds and passes its tests on its own.

Summary

serve_batching.py --backend native launches the Muse Glimmer server on the native engine.

  • Passes the worker --native-device and --native-kv-dtype.
  • Rejects what the native worker can't use, DFlash, the vision table and a --data-path, before starting the worker. Sessions and the prefix cache work as on the other backends.
  • BATCHING_SERVING.md covers the native export, build and launch.
python -m executorch.examples.models.muse_glimmer.serving.serve_batching \
  --backend native --worker-bin .../muse_glimmer_batching_worker \
  --model-path mg.ptn --tokenizer-path <mg-hf>/tokenizer.json \
  --hf-tokenizer <mg-hf> --max-context 8192

Test plan

  • test_serve_batching.py (33 pass): native options forwarded, omitted for other backends, DFlash, vision and a data path rejected.
  • Served the full Muse-Glimmer-30B Q4_K_M export on an M4 Max (Metal) through /v1/chat/completions with --max-sessions 4 --prefix-cache-entries 4:
    • three concurrent requests ran at 31.2 tokens/s against 23.5 tokens/s one at a time, with answers identical to the sequential ones ("The capital of France is Paris.", "Red\nBlue\nYellow", "144");
    • a second request sharing an 848-token prompt reused 842 cached tokens and answered correctly.

Authored with Claude Code.

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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24957

Note: Links to docs will display an error until the docs builds have been completed.

❌ 63 New Failures, 4 Cancelled Jobs, 1 Pending, 4 Unrelated Failures, 23 Unclassified Failures

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@meta-cla meta-cla Bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Nov 12, 2026
@metascroy
metascroy force-pushed the offgraph-kv-mg-native-serve branch from d06a2ad to a603733 Compare November 13, 2026 00:57
@metascroy
metascroy force-pushed the offgraph-kv-mg-native-executor branch from b1a668c to 4cfa8c9 Compare November 13, 2026 00:57
@metascroy
metascroy force-pushed the offgraph-kv-mg-native-serve branch from a603733 to fe1927e Compare November 13, 2026 21:57
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metascroy force-pushed the offgraph-kv-mg-native-executor branch from 4cfa8c9 to 5bf37b8 Compare November 13, 2026 21:58
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metascroy force-pushed the offgraph-kv-mg-native-executor branch from 5bf37b8 to 49d63b7 Compare November 14, 2026 00:18
@metascroy
metascroy force-pushed the offgraph-kv-mg-native-serve branch from fe1927e to 0f1cf5a Compare November 14, 2026 00:18
serve_batching.py takes --backend native and passes the worker the native
engine's --native-device and --native-kv-dtype. It rejects what the native
worker cannot serve, DFlash and the vision table, before starting the
worker. BATCHING_SERVING.md covers the native export, build and launch.

Served the full Muse-Glimmer-30B-KQuant-17GB-Q4_K_M export on Metal with
four sessions and a prefix cache: three concurrent requests ran at 31
tokens/s against 23.5 one at a time, with the same answers, and a repeated
848-token prompt reused 842 cached tokens.

This branch had an error being deployed

1 failed deployment
cadence — 25cd7145 Deployed Nov 14, 2026 by metascroy via hifi1s-build / hifi1s #38346
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