mirror of https://github.com/maderix/ANE.git
Add cross-generation ANE benchmark report from issue #3
Community-submitted results for M1 Pro/Max, M3 Pro, M4 Pro/Max, M5. Includes training performance, peak throughput, MIL compatibility matrix, and structured JSON data.
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# Apple Neural Engine — Cross-Generation Benchmark Report
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Community-submitted benchmark data from [Issue #3](https://github.com/maderix/ANE/issues/3).
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All results use Stories110M (12-layer transformer, 109M params, dim=768, seq=256).
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## Training Performance (Static Pipeline)
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```
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Chip ms/step ANE ms Compile/10 ANE TFLOPS Util% Contributor
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─────────────────────────────────────────────────────────────────────────────────
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M1 Pro 148-163 32-35 7.9-8.5s 0.57-0.63 3.6-4.0 @moriwang
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M1 Max 143-167 35-45 ~7.1s 0.54-0.65 3.4-4.1 @andyg5000
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M3 Ultra* 91 ~10 ~3.7s 0.88 5.6 (repo ref)
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M4 Pro 69-73 8.9 ~3.5s 1.28 8.1 @srt54558
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M4 Max 64 10.2 ~3.5s 1.45 9.2 @SethBurkart123
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M5 101-120 9.1-9.8 3.2-3.4s 0.77-0.91 4.9-5.8 @GitBubble
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```
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*M3 Ultra = reference platform this project was developed on.
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## Peak ANE Throughput (inmem_peak, 128x conv 512ch sp64)
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```
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Chip TFLOPS Rated TOPS Utilization
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───────────────────────────────────────────────────
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M1 Pro FAIL 11 - (MIL compat issue)
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M1 Max FAIL 11 - (MIL compat issue)
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M3 Pro 9.98 15.8 63%
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M4 Pro 12.57 38 33%
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M4 Max 10.93 38 29%
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M5 12.17 ~19* 64%
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M5 (other) 12.44 ~19* 65%
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```
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*M5 ANE TOPS not officially disclosed; ~19 TOPS estimated from measured peak.
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## Comparative Chart
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```
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ANE Training Speed (ms/step, lower is better)
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══════════════════════════════════════════════════════════════
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M1 Pro ████████████████████████████████████████░░░░ 148-163 ms
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M1 Max ██████████████████████████████████████░░░░░░ 143-167 ms
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M3 Ultra ██████████████████░░░░░░░░░░░░░░░░░░░░░░░░░ 91 ms
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M4 Pro ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 69-73 ms
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M4 Max ████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 64 ms
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M5 ████████████████████████░░░░░░░░░░░░░░░░░░░░ 101-120 ms
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0 50 100 150 200
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Peak ANE Throughput (TFLOPS, higher is better)
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══════════════════════════════════════════════════════════════
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M1 Pro FAIL (MIL compat)
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M1 Max FAIL (MIL compat)
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M3 Pro ████████████████████░░░░░░░░░░░░░░░░░░░░░░░░ 9.98
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M4 Pro ████████████████████████████████░░░░░░░░░░░░░ 12.57
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M4 Max ██████████████████████░░░░░░░░░░░░░░░░░░░░░░ 10.93
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M5 █████████████████████████░░░░░░░░░░░░░░░░░░░ 12.17
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0 3 6 9 12 15 18
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ANE Sustained Throughput (TFLOPS, 5s window)
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══════════════════════════════════════════════════════════════
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M3 Pro ██████████████████████████████████████████████ 15.04 (95.2%)
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0 3 6 9 12 15 18
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(Only M3 Pro submitted sustained benchmark)
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```
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## Key Findings
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### M1/M1 Pro/M1 Max
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- **Standalone benchmarks fail** — `ane_mil_gen.h` single-blob weight format rejected
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- **Training works** via `stories_mil.h` (separate per-matrix weight blobs)
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- ANE compiler handles weight blobs differently from M4+
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- Training at 148-167 ms/step, ~0.6 TFLOPS
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### M3 Pro
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- **Only ch=512 compiles** — 52 channel values tested (1-4096), only 512 accepted
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- Fixed 512-wide lane structure in SRAM tiling
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- **Peak: 16.77 TFLOPS** (106% of rated 15.8 TOPS) at 128x conv 512ch sp2048
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- **Sustained: 15.04 TFLOPS** over 5 seconds (95.2% utilization)
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- Spatial dimension is the key to peak throughput (sp64→sp2048 = 2x improvement)
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### M4 Pro / M4 Max
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- Flexible channel support (256/384/512/768+)
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- M4 Pro: peak 12.57 TFLOPS, training at 72.5 ms/step
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- M4 Max: peak 10.93 TFLOPS, training at 64 ms/step (fastest overall)
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- `sram_probe` and `inmem_bench` fail on M4 Pro (same MIL compat issue)
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### M5
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- Training works out of the box with existing `program(1.3)` MIL
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- Training speed 101-120 ms/step (slower than M4 Max, comparable to M3 Ultra)
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- Peak ANE throughput ~12.2-12.4 TFLOPS (similar to M4 Pro)
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- ANE appears to be same H16 family as M4
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- **M5 Pro/Max not yet benchmarked** — Fusion Architecture may change ANE behavior
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### Cross-Generation MIL Compatibility
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```
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Feature M1 M3 M4 M5
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─────────────────────────────────────────────────────────
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program(1.3) / ios18 PARTIAL YES YES YES
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Single-blob weights FAIL YES YES YES
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Per-matrix weight blobs YES YES YES YES
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Channel flexibility ? ch=512 FLEX FLEX
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BLOBFILE offset refs FAIL YES YES YES
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```
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## macOS Compatibility Issues
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- **macOS 26.x** — `[MLModel compileModelAtURL:]` broken for standalone benchmarks
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(fixed in PR #27: switched to in-memory MIL compilation)
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- **macOS 15.x** — Works for all M-series with correct MIL format
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- M1 generation requires `stories_mil.h` path, not `ane_mil_gen.h`
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## How to Contribute
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Run on your hardware and post results to [Issue #3](https://github.com/maderix/ANE/issues/3):
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```bash
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cd training && make train_large
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./train_large ane_stories110M_ckpt.bin 256 20 1e-4
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```
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Include: chip model, macOS version, full output with JSON lines.
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---
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*Report compiled 2026-03-04 from community submissions.*
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*Contributors: @SethBurkart123, @srt54558, @andyg5000, @moriwang, @D-Ogi, @GitBubble, @elijah-pelton*
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{
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"report_date": "2026-03-04",
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"source": "https://github.com/maderix/ANE/issues/3",
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"model": "Stories110M (12-layer transformer, 109M params)",
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"config": {"dim": 768, "hidden": 2048, "heads": 12, "seq": 256, "vocab": 32000, "layers": 12},
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"training_results": [
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{
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"chip": "M1 Pro",
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"cores": "10-core CPU",
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"ram_gb": 32,
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"macos": "15.0",
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"ms_per_step": [148, 163],
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"ane_ms": [32, 35],
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"compile_ms": [7900, 8500],
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"ane_tflops": [0.57, 0.63],
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"ane_util_pct": [3.6, 4.0],
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"benchmarks_pass": false,
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"notes": "Standalone benchmarks fail (MIL compat). Training works via stories_mil.h.",
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"contributor": "moriwang"
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},
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{
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"chip": "M1 Max",
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"cores": "10-core CPU",
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"ram_gb": 64,
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"macos": "15.6.1",
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"ms_per_step": [143, 167],
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"ane_ms": [35, 45],
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"compile_ms": [7100, 7100],
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"ane_tflops": [0.54, 0.65],
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"ane_util_pct": [3.4, 4.1],
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"benchmarks_pass": false,
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"notes": "Same MIL compat issue as M1 Pro.",
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"contributor": "andyg5000"
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},
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{
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"chip": "M3 Pro",
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"cores": "12-core CPU",
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"ram_gb": 36,
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"macos": "15.7.4",
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"peak_tflops": 16.77,
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"sustained_tflops": 15.04,
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"sustained_util_pct": 95.2,
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"channel_constraint": "ch=512 only",
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"notes": "Only ch=512 compiles. 52 values tested. Peak at 128x conv 512ch sp2048.",
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"contributor": "D-Ogi"
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},
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{
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"chip": "M4 Pro",
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"cores": "unknown",
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"ram_gb": null,
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"macos": null,
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"ms_per_step": [69, 73],
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"ane_ms": [8.9, 8.9],
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"compile_ms": [3465, 3465],
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"ane_tflops": [1.28, 1.28],
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"ane_util_pct": [8.1, 8.1],
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"peak_tflops_inmem": 12.57,
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"notes": "sram_probe and inmem_bench fail. inmem_peak and training work.",
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"contributor": "srt54558"
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},
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{
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"chip": "M4 Max",
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"cores": "unknown",
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"ram_gb": null,
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"macos": null,
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"ms_per_step": [64, 64],
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"ane_ms": [10.2, 10.2],
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"compile_ms": [3531, 3531],
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"ane_tflops": [1.45, 1.45],
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"ane_util_pct": [9.2, 9.2],
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"peak_tflops_inmem": 10.93,
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"notes": "Fastest training ms/step overall.",
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"contributor": "SethBurkart123"
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},
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{
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"chip": "M5",
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"cores": "10-core (4P+6E)",
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"ram_gb": 16,
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"macos": "26.3",
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"ms_per_step": [101, 120],
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"ane_ms": [9.1, 9.8],
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"compile_ms": [3200, 3400],
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"ane_tflops": [0.77, 0.91],
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"ane_util_pct": [4.9, 5.8],
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"peak_tflops_inmem": 12.44,
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"notes": "H16 ANE family (same as M4). Training works with existing program(1.3) MIL.",
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"contributor": "GitBubble"
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},
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{
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"chip": "M5",
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"cores": "unknown",
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"ram_gb": 32,
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"macos": "26.4",
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"peak_tflops_inmem": 12.17,
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"notes": "inmem_peak only, no training data submitted.",
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"contributor": "elijah-pelton"
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}
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],
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"neural_engine_specs": {
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"M1": {"cores": 16, "rated_tops": 11},
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"M2": {"cores": 16, "rated_tops": 15.8},
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"M3": {"cores": 16, "rated_tops": 15.8},
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"M4": {"cores": 16, "rated_tops": 38},
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"M5": {"cores": 16, "rated_tops": null, "estimated_tops": 19}
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}
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}
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