Independent benchmarks will determine whether omlx’s hybrid Apple Neural Engine and GPU prefill materially improves large quantized-model throughput on Apple Silicon despite increased peak memory use.

state: expiredheat: lowuncertainty: highconvergesscott: lowlocal-inference apple-silicon inference-optimizationomlxApple

What is this?

omlx is experimenting with dual-accelerator prompt processing on Apple Silicon: PR #2756 adds experimental dual-ANE/GPU processing for Qwen3.5, aiming to use the Apple Neural Engine alongside the GPU during prefill while GPU-based inference handles decode. The supplied snippets establish that long-prompt prefill and memory capacity are practical Apple Silicon constraints, but they do not provide independent benchmark results for this PR or quantify its throughput and peak-memory trade-off. One source instead argues that accelerator coordination and Core ML conversion can outweigh hybrid-prefill gains, so the claimed material improvement remains unverified here.

Why it matters to Scott

omlx’s explicit ANE/GPU placement, quantization, and memory trade-offs converge with Scott’s hardware-aware local-inference concept. However, the hits do not show Scott actively using Apple Silicon or omlx, and without independent benchmarks this remains another unverified implementation example rather than evidence likely to change what he builds or argues.

dev:concept.hardware-aware-local-inferenceradar:concept.local-inferenceradar:concept.apple-silicon-inferenceradar:concept.mlxradar:concept.inference-optimization

queries asked of Scott's wikis

Apple Silicon local inference optimization
ANE GPU hybrid prefill and decode
local LLM benchmark methodology and sustained throughput
unified memory trade-offs for quantized models
multi-accelerator inference orchestration
local inference economics versus NVIDIA GPUs

Measured heat

no measured readings yet — the hourly heat pass fills this in

How the heat travelled

no chain yet — the hourly chain pass fills this in

Evidence (3) — ⭐ canonical anchor

source	object	author	score	comments
🟠 reddit	Someone apparently cracked dual ANE+GPU prefill on apple silicon LocalLLaMA	bakawolf123	11	2
🟧 echo.github ⭐	The original primary source is PR #2756, titled “feat(qwen3.5): add experimental dual-ANE/GPU prompt processing.” It describes two ANE proce	Fabian (onthehub97)	—	—
🟠 reddit	Been tweaking my Qwen 3.8 setup, up to 45+ steady T/ps at 8bit quant. Realised I'm now top T/ps for this model+ctx across all benchmarked M-series chips. Full args linked below, happy to discuss as this was a pain of trial and error. LocalLLaMA	Adventurous_Cat_1559	35	37

Interpretation history

2026-08-25T15:46:53Z
Repeated reobservation has produced only minor engagement and no controlled benchmark isolating hybrid ANE/GPU prefill or its memory cost. The implementation remains practically corroborated, but this episode has gone dormant and should reopen only on substantive independent benchmarking.
2026-08-23T15:38:42Z
The refreshed thread is continued amplification and setup discussion, not evidence isolating hybrid ANE/GPU prefill or quantifying its memory cost. Practical gains remain independently supported, but their attribution and generality are unchanged.
2026-08-21T14:33:40Z
The refreshed comments add no isolated benchmark or new hardware coverage, so they do not improve attribution of the observed gains to hybrid ANE/GPU prefill. The case remains independently supported as a practical implementation, but repeated setup discussion is now amplification rather than progress.
2026-08-21T05:24:38Z
The refreshed discussion adds no controlled benchmark isolating hybrid ANE/GPU prefill from speculative prefill, MTP, and other tuning. The practical implementation remains independently supported, but attribution, hardware generality, and peak-memory trade-offs are still unresolved.
2026-08-21T02:24:04Z
Refreshed comments remain setup discussion and optimization advice, not a controlled benchmark isolating hybrid ANE/GPU prefill. The implementation has independent practical support, but attribution, hardware generality, and the memory trade-off remain unsettled.
2026-08-20T23:33:29Z
The community M3 Ultra run strengthens evidence that omlx’s broader optimized stack can deliver practical local-inference gains, but its combined use of ANE prefill, speculative prefill, MTP, and system tuning prevents attribution to hybrid ANE/GPU processing. The case remains corroborated but not accelerating pending controlled benchmarks that isolate throughput and memory effects.
2026-08-20T23:23:04Z
evidence attached: reddit.post.1vty1g4 — A detailed user benchmark provides useful independent evidence about omlx MTP, speculative prefill, and Apple Silicon inference speed.
2026-08-19T22:35:16Z
The previously observed M1 Pro result provides a second, independent line of evidence for a real prefill gain while reinforcing the substantial peak-memory cost. Generality across models and Apple hardware remains unsettled, and the latest change is only minor engagement rather than new benchmarking.
2026-08-19T22:29:03Z
grounded: converges/low — omlx’s explicit ANE/GPU placement, quantization, and memory trade-offs converge with Scott’s hardware-aware local-inference concept. However, the hits do not sh
2026-08-19T22:26:25Z
origin walked (codex/luna, conf 0.98): anchor reddit.post.1vt0g7a -> echo.github.307fb3667d by Fabian (onthehub97)
2026-08-19T22:25:03Z
case created — A reportedly released implementation makes a specific and independently testable Apple-Silicon inference-performance claim.

Decision trace

08-26 01:46expireRepeated reobservation has produced only minor engagement and no controlled benchmark isolating hybrid ANE/GPU prefill or its memory cost. The implementation remains practically corroborated, but this
08-26 01:46alert_silentNo consequential evidence arrived; a two-point engagement increase does not alter attribution, generality, or the unresolved throughput-memory trade-off.
08-26 01:46alert_routeNo consequential evidence arrived; a two-point engagement increase does not alter attribution, generality, or the unresolved throughput-memory trade-off.
08-24 01:38repriceThe refreshed thread is continued amplification and setup discussion, not evidence isolating hybrid ANE/GPU prefill or quantifying its memory cost. Practical gains remain independently supported, but
08-24 01:38alert_silentOnly engagement and routine discussion increased; no controlled throughput-and-peak-memory comparison arrived, so the case can wait for regular review.
08-24 01:38alert_routeOnly engagement and routine discussion increased; no controlled throughput-and-peak-memory comparison arrived, so the case can wait for regular review.
08-22 00:33repriceThe refreshed comments add no isolated benchmark or new hardware coverage, so they do not improve attribution of the observed gains to hybrid ANE/GPU prefill. The case remains independently supported
08-22 00:33alert_silentNo consequential new result arrived; routine review is sufficient until a controlled throughput-and-peak-memory comparison isolates hybrid ANE/GPU prefill from speculative prefill, MTP, and other tuni
08-22 00:33alert_routeNo consequential new result arrived; routine review is sufficient until a controlled throughput-and-peak-memory comparison isolates hybrid ANE/GPU prefill from speculative prefill, MTP, and other tuni
08-22 00:21sensor_dirtycomment_update
08-21 15:24repriceThe refreshed discussion adds no controlled benchmark isolating hybrid ANE/GPU prefill from speculative prefill, MTP, and other tuning. The practical implementation remains independently supported, bu
08-21 15:24alert_silentThis is repetitive setup and optimization discussion rather than a consequential new result; it can wait for routine review pending an isolated throughput-and-memory benchmark.
08-21 15:24alert_routeThis is repetitive setup and optimization discussion rather than a consequential new result; it can wait for routine review pending an isolated throughput-and-memory benchmark.
08-21 14:21sensor_dirtycomment_update
08-21 12:24repriceRefreshed comments remain setup discussion and optimization advice, not a controlled benchmark isolating hybrid ANE/GPU prefill. The implementation has independent practical support, but attribution,
08-21 12:24alert_silentNo consequential new result arrived; the refreshed discussion does not change the prior assessment and can wait for routine review.
08-21 12:24alert_routeNo consequential new result arrived; the refreshed discussion does not change the prior assessment and can wait for routine review.
08-21 11:21sensor_dirtycomment_update
08-21 10:21sensor_dirtycomment_update
08-21 09:33repriceThe community M3 Ultra run strengthens evidence that omlx’s broader optimized stack can deliver practical local-inference gains, but its combined use of ANE prefill, speculative prefill, MTP, and syst
08-21 09:33alert_silentThis benchmark was already assessed and does not isolate the hybrid implementation; the only further change is one additional comment, so there is no consequential delta that should interrupt the next
08-21 09:33alert_routeThis benchmark was already assessed and does not isolate the hybrid implementation; the only further change is one additional comment, so there is no consequential delta that should interrupt the next
08-21 09:23alert_silentA reproducible community run adds useful M3 Ultra configuration, throughput, thermal, and memory-pressure observations, but it is a single self-report combining ANE prefill with speculative prefill, M
08-21 09:23surface_candidateA reproducible community run adds useful M3 Ultra configuration, throughput, thermal, and memory-pressure observations, but it is a single self-report combining ANE prefill with speculative prefill, M
08-21 09:23alert_routeA reproducible community run adds useful M3 Ultra configuration, throughput, thermal, and memory-pressure observations, but it is a single self-report combining ANE prefill with speculative prefill, M
08-21 09:23attachA detailed user benchmark provides useful independent evidence about omlx MTP, speculative prefill, and Apple Silicon inference speed.
08-21 09:22propose_attachA detailed user benchmark provides useful independent evidence about omlx MTP, speculative prefill, and Apple Silicon inference speed.
08-20 19:21sensor_dirtyengagement_update
08-20 10:21sensor_dirtyengagement_update
08-20 08:35repriceThe previously observed M1 Pro result provides a second, independent line of evidence for a real prefill gain while reinforcing the substantial peak-memory cost. Generality across models and Apple har
08-20 08:35alert_silentThe substantive independent result was already assessed; the only new delta is a small engagement increase with no additional benchmark, implementation change, or consequence for Scott.
08-20 08:35alert_routeThe substantive independent result was already assessed; the only new delta is a small engagement increase with no additional benchmark, implementation change, or consequence for Scott.
08-20 08:33alert_silentA concrete user report adds early independent evidence of a roughly 19% prefill gain on an M1 Pro, while also confirming an approximately 2× peak-memory penalty. However, it is a single anecdotal benc
08-20 08:33surface_candidateA concrete user report adds early independent evidence of a roughly 19% prefill gain on an M1 Pro, while also confirming an approximately 2× peak-memory penalty. However, it is a single anecdotal benc
08-20 08:33alert_routeA concrete user report adds early independent evidence of a roughly 19% prefill gain on an M1 Pro, while also confirming an approximately 2× peak-memory penalty. However, it is a single anecdotal benc
08-20 08:29groundomlx’s explicit ANE/GPU placement, quantization, and memory trade-offs converge with Scott’s hardware-aware local-inference concept. However, the hits do not show Scott actively using Apple Silicon or
08-20 08:26promote_anchororigin walk conf 0.98
08-20 08:25createA reportedly released implementation makes a specific and independently testable Apple-Silicon inference-performance claim.