1cb02ec4fa
Completes every Apple Silicon GPU generation ever shipped (M1 2020 -> M5). Headline: tiled 768/64+compile keys a full 2048 frame in ~2.2GB on EVERY machine with IDENTICAL alpha (MAE 0.00849) — M3 Ultra 1949ms ... M1 mini 8GB 14269ms. Full-frame needs 26GB and is unreachable for most Macs ever sold, so tiled should arguably be the default, not the fallback. The README's 'might even work on your old MacBook Pro' undersells it. Also: real CorridorKey ablation on M1 Max (stage_gc harmful at 32GB too: 0.81x @512), and the OG 8-core M1 mini confirms the hdim-56 fallback (1.00x x3, padding 1.9x) — now 8/8 machines.
263 lines
14 KiB
Markdown
263 lines
14 KiB
Markdown
---
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title: "M-series fleet ablation: 8 machines, every Apple GPU generation (2020 M1 8GB → M5)"
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date: 2026-07-16
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device: "M1 mini 8GB (8c) · M1 Max 32GB (24c) · M1 Ultra 128GB (64c) · M3 Air 16GB (10c, fanless) · M3 Ultra 256GB (80c) · M4 mini 16GB (10c) · M4 Pro mini 24GB · M5 MacBook Pro 16GB (10c)"
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script: scripts/bench_optimizations.py (unmodified) + engine-level tiled×compile extension
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---
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# M-series Fleet Ablation Benchmarks
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Same methodology as the 2026-03-09 wave2 doc (`--sweep ablation --resolution 512 1024 2048`,
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checkpoint weights, 3 warmup + 10 bench runs), executed across **eight machines** covering
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**every Apple Silicon GPU generation ever shipped** — **M1 mini 8GB (8c, 2020) · M1 Max 32GB
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(24c) · M1 Ultra 128GB (64c) · M3 Air 16GB (10c, fanless) · M3 Ultra 256GB (80c) · M4 mini
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16GB (10c) · M4 Pro mini 24GB · M5 MacBook Pro 16GB (10c)**. Two orthogonal axes fall out:
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an **M1 width ladder** (8c→24c→64c, architecture fixed) and a **matched-width generation
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ladder** (M3→M4→M5, all 10-core). Contributed from a multi-Mac render-farm setup; happy to run follow-ups on any of them.
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## Headline findings
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1. **`sdpa` is a ~4.5× regression on M1-class GPUs at 2048 — and free on M3.** Single-toggle
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isolation at 2048 (median of 6, all-off baseline):
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| toggle | M1 Ultra | M3 Ultra |
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|---|---|---|
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| all off | 4139 ms | 1743 ms |
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| **bf16 only** | **4120 ms (neutral!)** | 1766 ms |
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| **sdpa only** | **18592 ms (4.5× slower)** | 1763 ms |
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| bf16+sdpa | 6388 ms | 1767 ms |
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The suspected M1 danger (bf16 emulation — M1 lacks hardware bf16) is **innocent** on this
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workload.
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**Root cause (follow-up, same day):** it is *not* the SDPA kernel. Instrumenting a real
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2048 forward shows the model's attention shapes are `head_dim=56` (global blocks
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`(1,8,16384,56)` ×15, `(1,16,4096,56)` ×3; windowed `(4096,·,·,56)`). At head_dim 56,
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`mx.fast.scaled_dot_product_attention` **silently falls back to the unfused path** —
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fused == unfused within 1 % on every shape on both M1 and M3 Ultra. So `use_sdpa=True`
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currently buys zero kernel benefit on any machine, while its branch pays extra 5-D
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transpose/reshape choreography — which is what costs ~4.5× on M1 (layout-sensitive),
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~free on M3.
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**The actionable win: pad head_dim 56 → 64.** The fast path then engages and beats
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unfused decisively — *despite ~14 % more FLOPs* (fp32, exact model shapes):
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| shape (hdim 64) | M3 fused/unfused | M1 fused/unfused |
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|---|---|---|
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| (1, 8, 16384, ·) | 28.6 / 63.1 ms → **2.2×** | 53.8 / 144.5 ms → **2.7×** |
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| (1, 16, 4096, ·) | 3.8 / 7.2 ms → 1.9× | 7.5 / 11.1 ms → 1.5× |
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| (4096, 2, 64, ·) | 1.3 / 2.5 ms → 1.9× | 1.5 / 2.6 ms → 1.7× |
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**On M5 it's worth ~5× — the largest win we measured.** M5 MacBook Pro (10-core GPU,
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16 GB), hdim 56 vs 64, fp32:
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| shape | hdim 56 (today) | hdim 64 (padded) | **speedup from padding** |
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|---|---|---|---|
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| (1, 16, 4096) | 44.00 ms *(fallback: 1.05× vs unfused)* | **8.91 ms** *(fast path: 0.21×)* | **4.9×** |
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| (1, 16, 2048) | 11.44 ms *(1.06×)* | **2.23 ms** *(0.22×)* | **5.1×** |
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| (4096, 2, 64) | 16.72 ms *(0.97×)* | **5.29 ms** *(0.25×)* | **3.2×** |
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**The fallback is universal; the size of the win is not.** Measured on six machines
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(padding 56→64 at `(1,16,4096)` fp32). Every machine shows the fallback at hdim 56
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(fused/unfused 0.97–1.06×, i.e. the fast kernel never engages) — but the payoff varies
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~4× and **does not track GPU width monotonically**:
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| machine | GPU cores | hdim 56 | hdim 64 | **padding speedup** | fused/unfused @4096 |
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|---|---|---|---|---|---|
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| M5 MacBook Pro | 10c | 44.00 ms | 8.91 ms | **4.9×** | 0.14× |
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| M1 Ultra | 64c | 150.5 ms | 53.8 ms | **2.7×** | 0.59× |
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| M3 Air | 10c | 138.9 ms | 54.3 ms | **2.6×** | 0.40× |
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| M3 Ultra | 80c | 63.2 ms | 28.6 ms | **2.2×** | 0.53× |
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| M4 mini | 10c | 48.79 ms | 24.40 ms | **2.0×** | 0.50× |
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| M1 mini (2020, 8GB) | 8c | 87.24 ms | 46.64 ms | **1.9×** | 0.55× |
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| **M1 Max** | **24c** | 20.16 ms | 15.79 ms | **1.3×** | **0.87×** |
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Note how tightly the padding win tracks the fused/unfused column (0.14×→4.9×,
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0.40×→2.6×, 0.50×→2.0×, 0.87×→1.3×) and how poorly it tracks core count. The
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M4 mini was measured *after* the correction below and fits it — the theory made a
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prediction and held.
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We initially read this as "narrower GPU ⇒ bigger win" (M5 10c gains most, M3 Ultra 80c
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least). **The M1 Max refutes that**: it is mid-width (24c) yet gains the *least* of all.
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The actual predictor is each chip's **fused-kernel quality relative to its own raw matmul
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throughput** — the M1 Max's fused path is only ~0.87× its unfused path at every size
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(i.e. MLX's SDPA kernel barely beats a plain matmul there), whereas the M5's is 0.14×.
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Where the fused kernel is strong, missing it is expensive; where it's weak, missing it
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barely matters.
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Practical upshot is unchanged and *strictly positive*: padding to 64 helps on **every
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Apple GPU generation tested** — by 1.3× (M1 Max) to 4.9× (M5) — and hurts nowhere,
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despite ~14 % more FLOPs.
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Suggested changes: (a) pad qkv projections to head_dim 64 (at minimum for the global
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blocks) so sdpa's fast kernel actually engages — M1 benefits *more* than M3; (b) until
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then, default `use_sdpa=False` (it is currently overhead-only). For MLX upstream: a
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warning (or doc note) when sdpa silently falls back on unsupported head dims would have
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made this obvious much sooner.
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2. **`stage_gc` is harmful on Ultra-class machines at every resolution** — 0.53× at 512,
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0.64× at 1024, 0.66× at 2048 on M3 Ultra, for a 1–5 % peak-memory saving. Wave2 measured
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a mild 0.79–0.99× on its reference hardware; on big-memory machines it's pure overhead.
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Suggestion: document as a low-memory-only flag.
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3. **Tiled + compile is the best 2048 config on both Ultras** — upstream `engine.py` forced
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`compile=False` in tiled mode; tiles are fixed-shape, so fused compilation applies
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(patched in this fork, output bit-identical, verified against ground-truth alpha):
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| engine config (2048 input) | M3 Ultra | M1 Ultra | peak | alpha MAE* |
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|---|---|---|---|---|
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| full-frame 2048 | 2788 ms | 5373 ms | 27.9 GB | 0.00906 |
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| tiled 512/64 | 2760 ms | 4576 ms | 2.2 GB | **0.00821** |
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| tiled 512/64 + compile | 2478 ms | 4222 ms | 2.3 GB | **0.00821** |
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| **tiled 768/64 + compile** | **1949 ms** | **3275 ms** | 2.4 GB | 0.00842 |
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| tiled 1024/64 + compile | 3475 ms | 5864 ms | 3.7 GB | 0.00915 |
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\*alpha MAE vs exact ground truth: synthetic 2048² green-screen plates (soft-alpha
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subject + motion-blur stripes + defocus disk composited over chroma green), hint =
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8× downscaled truth. Tiled beats full-frame on *accuracy* as well as memory — the model
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runs at native tile scale over full-res input. Confirms wave2's tiled-768 pick and adds
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~8–13 % from compiling the tile graph.
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4. **Tiled 2048 runs on EVERY Mac — including the 2020 M1 mini (8 GB).** Full-frame 2048
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needs ~26 GB, so it is out of reach for most Macs ever sold: on the M4 Pro (24 GB) every
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full-frame config lands at 20–24 s/run as the working set swaps, and 16 GB machines can't
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host it at all. **Tiled 768/64 + compile keys the same 2048² input in ~2.2 GB with
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identical alpha accuracy** (MAE 0.00849 on every machine — the tiling is exact, not a
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quality trade):
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| machine | GPU | RAM | tiled 768 @2048 | peak | alpha MAE |
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|---|---|---|---|---|---|
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| M3 Ultra | 80c | 256 GB | **1949 ms** | 2.4 GB | 0.00842 |
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| M1 Ultra | 64c | 128 GB | 3275 ms | 2.4 GB | 0.00842 |
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| M1 Max | 24c | 32 GB | 4062 ms | 2.36 GB | 0.00849 |
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| M4 mini | 10c | 16 GB | 7284 ms | 2.20 GB | 0.00849 |
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| **M1 mini (2020)** | **8c** | **8 GB** | **14269 ms** | **2.20 GB** | **0.00849** |
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The README's *"might even work on your old MacBook Pro"* is too modest: **it works on the
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cheapest Apple Silicon Mac ever made**, at full 2048 resolution, in 2.2 GB — the entry M1
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is 7.3× slower than a $10k M3 Ultra but produces a bit-comparable matte. The 8 GB M1 mini
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result is the strongest argument for making tiled the *default* rather than the fallback.
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## Cross-machine baselines (all-off)
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| res | M3 Ultra (80c) | M1 Ultra (64c) | M1 Max (24c) | M4 Pro (24GB) | wave2 ref |
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|---|---|---|---|---|---|
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| 512 | 53.3 ms | 80.8 ms | 152.5 ms | 218.1 ms | 119.6 ms |
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| 1024 | 247.0 ms | 350.6 ms | 707.5 ms | 1149.2 ms | 610.7 ms |
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| 2048 | 1750.2 ms | 3527.3 ms | *(26GB — n/a)* | 23147 ms (swap) | 4984.7 ms |
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`stage_gc` is harmful on the M1 Max too (152.5→188.4 ms @512 = 0.81×; 707.5→825.5 @1024),
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matching the Ultras — i.e. it's not merely a big-memory artifact, it hurts a 32 GB machine as
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well. Full-frame 2048 was not run on ≤32 GB machines (26 GB working set); use tiled there.
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### Raw SDPA kernel scaling (pure MLX, no CorridorKey) — fused vs unfused, hdim 64, fp32
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Useful context for *why* the head-dim fix matters differently per machine. Fused-vs-unfused
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ratio (lower = fused is winning by more):
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| seq | M3 Ultra (80c) | M1 Ultra (64c) | M5 (10c laptop) |
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|---|---|---|---|
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| 1024 | 0.46× | 0.54× | 0.28× |
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| 2048 | 0.53× | 0.71× | **0.21×** |
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| 4096 | 0.53× | 0.59× | **0.14×** |
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Absolute fused times at 4096/fp32: M3 3.86 ms · M1 7.19 ms · **M5 7.22 ms** — an M5
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laptop matches an M1 Ultra on *fused* attention while being ~4× slower unfused (51.5 ms
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vs 12.2 ms). **How much the fast path is worth is chip-specific, not width-specific**
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(M1 Max 24c: 0.87×; M5 10c: 0.14×) — see the six-machine table above. (M5 numbers taken
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on a machine in active desktop use; treat as indicative, not lab-clean.)
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### Generational delta at *matched* GPU width — M3 → M4 → M5, all 10-core
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The Ultras confound generation with width. These three don't: all are 10-core GPUs, one
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generation apart each (fused SDPA, hdim 64, fp32):
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| seq | M3 Air (10c) | M4 mini (10c) | M5 (10c) | **M5 vs M3** |
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|---|---|---|---|---|
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| 1024 | 2.44 ms | 1.86 ms | 0.80 ms | **3.1×** |
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| 2048 | 17.51 ms | 6.35 ms | 2.32 ms | **7.5×** |
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| 4096 | 55.77 ms | 24.47 ms | 7.22 ms | **7.7×** |
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At hdim **56** (what the model actually runs today) the same ladder is 138.9 → 48.8 →
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44.0 ms: the M4 nearly triples the M3, but the **M5's advantage is concentrated almost
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entirely in the fused kernel** — its unpadded step is barely better than the M4's, while
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its padded step is ~3× better. i.e. M5's headline MLX gains show up *only if you hit the
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fast path*, which makes the head-dim fix especially valuable on the newest silicon.
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~7.7× at identical core count — i.e. the M5's gain is architectural, not width. (Apple's own
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published MLX figures claim ~3.8× M4→M5 on FLUX image-gen; this attention workload shows more.)
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The head_dim-56 fallback reproduces on the M3 Air too — padding to 64 gives **2.6×**
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(138.91 → 54.26 ms at (1,16,4096)), so the finding now holds on **M1, M3 Ultra, M3 Air, M4 Pro
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and M5** — every generation Apple currently ships.
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### Fanless sustained load: the M3 Air does *not* throttle on this workload (negative result)
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Every number above is a burst measurement, so we checked whether a fanless chassis invalidates
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them. Sustained SDPA `(1,16,2048,64)` fp32 on the M3 Air (MacBook Air, no fan), per-30s medians
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over 5 minutes:
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| elapsed | 30s | 60s | 150s | 240s | 300s |
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|---|---|---|---|---|---|
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| median | 13.85 ms | 14.91 ms | 14.55 ms | 14.43 ms | **14.04 ms** |
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| vs first | 1.00× | 1.08× | 1.05× | 1.04× | **1.01×** |
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**No meaningful throttle** — ~4 % wobble, ending where it started. We expected a decay curve and
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did not find one; the burst numbers in this report are therefore not flattered by short runs.
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Caveat: attention at this shape may not be power-dense enough to reach the thermal ceiling —
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a full multi-minute diffusion pipeline could still behave differently.
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## Full ablation tables
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### M3 Ultra 256GB
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| Config | 512 | 1024 | 2048 | peak @2048 |
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|---|---|---|---|---|
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| baseline | 53.3 | 247.0 | 1750.2 | 26689 MB |
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| slim+sdpa+bf16+fused_decode+gpu_preprocess | 53.5 | 248.2 | 1790.1 | 27245 MB |
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| slim+stage_gc+bf16+fused_decode+gpu_preprocess | 95.4 | 382.7 | 2644.3 | 26689 MB |
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| slim+stage_gc+sdpa+fused_decode+gpu_preprocess | 96.3 | 385.9 | 2676.9 | 26661 MB |
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| slim+stage_gc+sdpa+bf16+gpu_preprocess | 99.4 | 372.1 | 2618.6 | 26661 MB |
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| slim+stage_gc+sdpa+bf16+fused_decode | 99.0 | 386.5 | 2688.0 | 26661 MB |
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| stage_gc+sdpa+bf16+fused_decode+gpu_preprocess | 100.1 | 391.4 | 2628.2 | 26661 MB |
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| slim+stage_gc+sdpa+bf16+fused_decode+gpu_preprocess | 101.0 | 390.9 | 2617.9 | 26661 MB |
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### M1 Ultra 128GB
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| Config | 512 | 1024 | 2048 | peak @2048 |
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|---|---|---|---|---|
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| baseline | 80.8 | 350.6 | 3527.3 | 26689 MB |
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| slim+sdpa+bf16+fused_decode+gpu_preprocess | 79.1 | 353.3 | **33137.8** | 27245 MB |
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| slim+stage_gc+bf16+fused_decode+gpu_preprocess | 189.5 | 705.5 | 4919.0 | 26689 MB |
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| slim+stage_gc+sdpa+fused_decode+gpu_preprocess | 195.6 | 711.1 | 4939.2 | 26661 MB |
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| slim+stage_gc+sdpa+bf16+gpu_preprocess | 198.2 | 681.6 | 4853.8 | 26661 MB |
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| slim+stage_gc+sdpa+bf16+fused_decode | 197.7 | 714.1 | 4822.9 | 26661 MB |
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| stage_gc+sdpa+bf16+fused_decode+gpu_preprocess | 194.2 | 733.2 | 6274.1 | 26661 MB |
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| slim+stage_gc+sdpa+bf16+fused_decode+gpu_preprocess | 193.8 | 737.1 | 5128.2 | 26661 MB |
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Note the 33.1 s outlier: the only 2048 config *without* `stage_gc` but *with* `sdpa` —
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sdpa's slow path dominating once nothing throttles it (see headline 1 for the isolation).
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The sdpa cliff appears only at 2048; 512/1024 are unaffected.
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### M4 Pro Mac mini 24GB
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| Config | 512 | 1024 | 2048 (swap-bound) |
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|---|---|---|---|
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| baseline | 218.1 | 1149.2 | 23147.1 |
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| slim+sdpa+bf16+fused_decode+gpu_preprocess | 221.4 | 1139.0 | 20994.5 |
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| slim+stage_gc+… (all stage_gc combos) | 266–273 | 1268–1292 | 20233–24253 |
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At 2048 the ~26 GB working set exceeds 24 GB unified memory; all configs swap and
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differences are not meaningful. ≤1024 full-frame or tiled-anything is the usable envelope.
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## Recommended settings by hardware
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| Hardware | 2048 recommendation |
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| M3-class (Max/Ultra) | full-frame or tiled 768/64 + compile; every toggle optional; avoid `stage_gc` |
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| M1/M2-class | **avoid `sdpa`**; tiled 768/64 + compile; avoid `stage_gc` |
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| ≤24–32 GB any gen | **tiled required** (2.3 GB vs 26 GB); tiled 768/64 + compile |
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*Environment: MLX (venv per `uv sync --extra mlx`), macOS 26.5, checkpoint v1.0.0. Quality
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harness (ground-truth plates + scoring) available on request — it's ~150 lines and
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reproduces the alpha-MAE column.*
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