# ANE weight-read bandwidth by chip > Method: anemll-bench loads prebuilt ANEMLL Core ML bundles (llama_lm_head 1050.67 MB, llama_lm_head_lut6 396 MB, DeepHermes_lm_head), runs N predict() calls (300 in recent reports) and reports size / mean latency as "throughput". It only works on ANEMLL-converted models, not arbitrary Hugging Fac... Parent: [Mac local LLMs: ANE and Core ML LLMs](https://llms-explorer.com/tree/mac-local-llms-ane-and-coreml-llm/) · 1 facets · 58 facts · page: https://llms-explorer.com/tree/ane-weight-read-bandwidth-by-chip/ ## Facts - Method: anemll-bench loads prebuilt ANEMLL Core ML bundles (llama_lm_head 1050.67 MB, llama_lm_head_lut6 396 MB, DeepHermes_lm_head), runs N predict() calls (300 in recent reports) and reports size / mean latency as "throughput". It only works on ANEMLL-converted models, not arbitrary Hugging Face models; only memory bandwidth is benchmarked (no compute or latency suite). — source: `asserted` - Single-model figure uses llama_lm_head only; dual-model tests run two bundles concurrently on separate Core ML instances and sum their throughput. — source: `asserted` - Measurement artefact (Oct 2026): on M6 16 GB the published 133.9 GB/s is wall-clock predict() throughput. ANE firmware task timestamps put actual weight read near ~150 GB/s. When ANE task duration falls to about 350 microseconds, the waiting CPU drops into a deeper idle state, which delays driver completion and user-space wake-up, so wall-clock throughput understates ANE bandwidth. The 16 GB vs 32 GB M6 gap (134 vs 152) is therefore not a memory-config ANE deficit. — source: `asserted` - Apple's own primary description (June 2022, ane_transformers article, Principle 4): many Transformer configs are bandwidth-bound on the ANE at short sequence length because large parameter tensors are fetched from memory and applied to too few inputs; latency stayed roughly constant across sequence 32/64/128 while compute quadrupled. Apple's remedies: raise batch size or shrink parameters via quantization or pruning. — source: `asserted` - Why a flat ~150 GB/s ceiling is plausible (inference, not measured): the ANE is a fixed-function graph engine fed through its own DMA path, not through the GPU's wide fabric; its figure tracked the generation (ANE tier), not the SoC's memory controller count. — source: `asserted` - Dispatch cost is separate from bandwidth: direct _ANEClient dispatch is ~0.095 ms (XPC + IOKit); Orion measures ~2.3 ms IOSurface round trip per dispatch on GPT-2 124M and ~5.78 ms gap per token across 12 layers on M4 Max. This is why small decode graphs cannot reach the weight-read ceiling. — source: `asserted` - 2022-06: Apple ml-ane-transformers article states bandwidth-boundness on the ANE as a design principle. — source: `asserted` - 2025-05 (HN, smpanaro, ANEMLL/ANE tinkerer): "ANE has a much lower ceiling than CPU/GPU (yes, despite unified memory)"; chunking is fine as long as chunks fit the ANE cache; M1 cache limit 3-4 GB, higher on M2+. — source: `asserted` - 2026-02/03: maderix reverse-engineering series and the Orion arXiv paper (2603.06728) measure M4/M4 Max ANE compute, SRAM, dispatch; neither publishes a DRAM bandwidth figure. — source: `asserted` - 2026-09: anemll-bench README adds M5, M5 Pro, M5 Ultra and M6 rows; Results.MD drops a historical latency column (mixed lut6 and full model artifacts) and separates end-to-end predict() from firmware weight-read. — source: `asserted` - x86_64 Python under Rosetta silently loses the ANE: "ANE Hardware Available: False", 100-500 ms inference vs 7-20 ms native; the bench now fails early with "ANE ACCESS BLOCKED". — source: `asserted` - Several rows are single contributor reports without run count or warmup (M5 Pro, M5 Ultra, M6 32 GB); M5 Ultra (36C CPU / 80C GPU / 256 GB) and M6 32 GB latency rows are flagged "run count and warmup policy not stated". — source: `asserted` - Only the M3 Pro and M3 Max rows are missing from the matrix (as of the Results.MD banner). — source: `asserted` - Core ML overhead: maderix measures Core ML adding 2-4x over direct _ANEClient on small ops, so predict()-based figures through Core ML can understate hardware. — source: `asserted` - ANE bakes weights at compile time (Orion), so "weight read" is a stream of compiled constants from DRAM, not a general tensor load. — source: `asserted` - Dual clusters. Results.MD prose for M5 Max says near-identical combined throughput (147.62 vs ~147 GB/s) "demonstrat[es] efficient bandwidth utilization across both ANE clusters". The same numbers show no gain over one model (each parallel model gets half: 74 + 74), which supports a shared ceiling, not two usable clusters. README still frames dual testing as "essential to evaluate the dual ANE clusters" on M1-M3 Ultra. Both framings sit side by side in the source; the table supports only "no aggregate gain". — source: `asserted` - INT8: HN ANEMLL author said M4/A17 INT8 runs at 2x FP16; maderix measured INT8 = FP16 compute (INT8 only saves weight bytes). Different claims (op rate vs memory); not averaged. — source: `asserted` - Hybrid strategy: maderix suggested ANE prefill + SME (CPU) decode; a reader (Evan R., Mar 2026) argued decode belongs on the GPU because KV cache lives in DRAM and GPU has more DRAM bandwidth; the author replied that full KV cache cannot stay in SRAM, so decode on CPU/GPU makes sense. — source: `asserted` - zozbot234 (HN): ANE benefit is prefill and power, not speed, because padding low-bit LLM weights to FP16/INT8 wastes the bandwidth that sets token rate; smpanaro: bandwidth ceiling, not padding, is the binding limit. — source: `asserted` - No independent (non-ANEMLL) measurement of ANE DRAM bandwidth exists in sources read; all per-chip figures come from one project's bundles on contributor machines. — source: `asserted` - Whether the ~150 GB/s plateau is an ANE DMA limit, a fabric limit or a firmware QoS cap is not documented. — source: `asserted` - Whether Pro/Max/Ultra ANEs have more than one cluster that Core ML can schedule onto is unresolved by the bench; M3 Pro/Max rows would help. — source: `asserted` - Why GPU decode on Pro/Max/Ultra beats ANE: stated cause is bandwidth (GPU 307-1200 GB/s vs ANE ~150), but no source ran both engines on the same model with power logging on M5 Pro/Max/Ultra. — source: `asserted` - anemll-bench measures throughput as model size divided by predict() time on prebuilt ANEMLL LM-head bundles and benchmarks only memory bandwidth in its current release. — [source](https://github.com/Anemll/anemll-bench) - anemll-bench works only with ANEMLL-converted models (llama_lm_head, llama_lm_head_lut6, DeepHermes_lm_head), not generic Hugging Face models. — [source](https://github.com/Anemll/anemll-bench) - anemll-bench recent reports use 300 runs; the M6 16 GB report is from 2026-09-30, repo ffc68fc, macOS 27.0.1. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - On M6 16 GB the bench shows llama_lm_head 7.85 ms / 133.92 GB/s, lut6 3.27 ms / 120.96 GB/s, DeepHermes 7.69 ms / 136.66 GB/s, all labelled end-to-end predict() throughput. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - Results.MD says ANE firmware task timestamps put M6 weight-read near ~150 GB/s, and that around 350 microseconds of ANE task duration the waiting CPU enters a deeper idle state that slows driver completion and wake-up. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - Results.MD states the 16 GB vs 32 GB M6 gap is not a 10-12% ANE deficit versus the 32 GB part. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - M6 16 GB dual-model run: solo 133.47 and 131.79 GB/s, parallel 70.84 and 68.09, combined 135.94 GB/s. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - M5 Max dual-model run: solo 148.39/148.57 GB/s, parallel 74.06/73.94, combined 147.62; M3 Ultra parallel 60.15/61.17, combined 120.29. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - Solo llama_lm_head latencies: M3 Ultra 8.74 ms, M5 Max 7.08, M5 Pro 7.00, M5 Ultra 6.99, M6 32 GB 6.91, M6 16 GB 7.85 (1050.67 MB model). — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - Results.MD removed its historical cross-chip latency column because it mixed llama_lm_head_lut6 and full llama_lm_head artifacts; a matching-model campaign is required for a valid latency chart. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - Results.MD tiers: Tier 1 ~120-152 GB/s (M6, M5 Ultra/Pro/Max, M4 Pro, M3 Ultra, M4 Max); Tier 2 ~60-70 (M5, M4, M3, M2 family, M1); Tier 3 ~55 (M1 Pro/Max/Ultra, no scaling across variants). — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - Results.MD states the ANE does not scale linearly with system memory bandwidth, and that substantial ANE architectural improvement began with M3 Ultra / M4 Pro-Max. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - anemll-bench auto-detects chip and recommends dual-model testing for M1/M2/M3 Ultra ("to evaluate the dual ANE clusters") and recommends it for M4 Pro/Max. — [source](https://github.com/Anemll/anemll-bench) - The README ANE-utilization table: M6 32 GB 152 of 170 GB/s (89%), M5 Pro 150 of 307 (49%), M4 Pro 126 of 273 (46%), M5 46%, M3 63%, M2 Pro 31%, M2 Ultra 8%, M1 Ultra 7%. — [source](https://github.com/Anemll/anemll-bench) - The matrix of requested submissions marks M3 Pro and M3 Max as still missing. — [source](https://github.com/Anemll/anemll-bench) - Results.MD prose for M5 Max claims the unchanged combined throughput demonstrates efficient bandwidth utilization "across both ANE clusters"; the table itself shows each parallel model at half the solo rate. — [source](https://github.com/Anemll/anemll-bench/blob/main/Results.MD) - Apple's 2022 ane_transformers article says many Transformer configs are bandwidth-bound on the ANE at short sequence lengths, with latency constant across sequence 32/64/128 at batch 1 while compute quadruples. — [source](https://machinelearning.apple.com/research/neural-engine-transformers) - Apple's recommended escapes from ANE bandwidth-boundness are larger batch, or smaller parameter tensors via quantization or pruning. — [source](https://machinelearning.apple.com/research/neural-engine-transformers) - Apple states the last axis of an ANE buffer is unpacked, contiguous and 64-byte aligned, so a singleton last axis is padded to 64 bytes. — [source](https://machinelearning.apple.com/research/neural-engine-transformers) - Apple's M5 release describes a "faster 16-core Neural Engine" without giving ANE TOPS or memory bandwidth; the AI speedup headline (over 4x peak GPU compute) is the GPU Neural Accelerators. — [source](https://www.apple.com/newsroom/2025/10/apple-unleashes-m5-the-next-big-leap-in-ai-performance-for-apple-silicon/) - Apple's MLX-on-M5 note gives M4 120 GB/s and M5 153 GB/s system memory bandwidth and says token generation is bandwidth-bound while TTFT is compute-bound. — [source](https://machinelearning.apple.com/research/exploring-llms-mlx-m5) - M5 base: ANE weight-read 70 GB/s vs 153 GB/s system bandwidth in Apple's figure (about 46%), so the base-chip ANE can use roughly half the DRAM stream the GPU sees. — source: `asserted` - Orion (arXiv 2603.06728) measures on M4 Max: ANE GPT-2 124M decode 170 tok/s vs CPU decode 283 tok/s, because of ~2.3 ms per-dispatch IOSurface round trip. — [source](https://arxiv.org/html/2603.06728v1) - Orion states the GPU via MLX or Metal currently achieves higher absolute LLM throughput than the ANE on M4 Max, and lists zero idle power, GPU/CPU left free, and 33.8x faster large-vocabulary softmax than CPU as ANE advantages. — [source](https://arxiv.org/html/2603.06728v1) - Orion states the ANE bakes weights into the compiled program at compile time and that its results were validated on M4 Max only. — [source](https://arxiv.org/html/2603.06728v1) - Orion reports ANE evaluation queue depth 127 and dispatch overhead ~0.095 ms (XPC + IOKit) for M4 Max (H16). — [source](https://arxiv.org/html/2603.06728v1) - maderix measured Core ML adding 2-4x overhead over direct _ANEClient calls on small ops, with the gap narrowing when ANE compute time dominates. — [source](https://maderix.substack.com/p/inside-the-m4-apple-neural-engine-615) - maderix measured a single matmul using only ~30% of ANE peak, and recommends 16-64-op deep graphs and 1x1 conv over matmul (matmul ~3x slower). — [source](https://maderix.substack.com/p/inside-the-m4-apple-neural-engine-615) - maderix states the ANE's M4 codename is H16G, with independent DVFS channels (ANE_ADCLK_TRIG, ANE_PPT_TRIG and others in IOReportLegend) and hard power gating to 0 mW idle. — [source](https://maderix.substack.com/p/inside-the-m4-apple-neural-engine) - maderix's measured ANE efficiency: ~6.6 TFLOPS/W peak versus ~1.0 for the M4 GPU (GPU figure stated approximate). — [source](https://maderix.substack.com/p/inside-the-m4-apple-neural-engine-615) - maderix's series was written as a human plus Claude Opus 4.6 collaboration on an M4 Mac Mini, and publishes no DRAM-bandwidth number for the ANE. — [source](https://maderix.substack.com/p/inside-the-m4-apple-neural-engine) - maderix's repo includes an sram_bench.m probe for ANE SRAM bandwidth and notes INT8 activations halve L2 SRAM bandwidth between tiles, while int8 weights are dequantized to fp16 at compile time. — [source](https://github.com/maderix/ANE) - HN (smpanaro, May 2025): the main bottleneck for transformers is memory bandwidth and the ANE "has a much lower ceiling than CPU/GPU (yes, despite unified memory)"; chunking is beneficial while chunks fit the ANE cache, which is 3-4 GB on M1 and higher on M2+. — [source](https://news.ycombinator.com/item?id=43879702) - HN (zozbot234, May 2025): padding low-bit LLM weights to FP16/INT8 for the ANE slashes effective memory-bandwidth use, so ANE benefit is prefill power, not speed. — [source](https://news.ycombinator.com/item?id=43879702) - smpanaro more-ane-transformers discussion: at GPT-2 scale the ANE bottleneck is weight-loading memory bandwidth, so Apple's split-softmax speedup did not show up. — [source](https://github.com/smpanaro/more-ane-transformers/discussions/3)