# MiniMax H3 — measured benchmarks

Measured 2026-08-06 with `bench/minimax_bench.py` through the ComfyUI API.
Raw data: `bench/results.jsonl` (B200), `bench/4090/`, `bench/4000/`.
Regenerate the tables with `python3 bench/summarize.py`.

**Method.** ComfyUI 0.30 headless; template-reference sampling everywhere:
`res_multistep` sampler, `simple` scheduler, **20 steps**, no CFG, 24 fps,
`nvfp4_awq` Qwen3-VL-32B text encoder, fp16 video VAE + fp32 audio VAE.
Times are ComfyUI execution time (`execution_start`→`execution_success`), which
excludes client/queue overhead (wall time tracked separately in the raw data;
typically +1–2 s). "Warm" = model already resident; first-load runs are
recorded but excluded from the means. Repeated configs use distinct seeds so
ComfyUI's node cache can't short-circuit. Reference inputs are real images
(film stills, anime characters, forests/volcanos, up to 23040×3840 —
`scripts/pod/fetch-bench-images-v2.py`).

Hardware: **B200** 180 GB (US, $6.94/hr) · **RTX 4090** 24 GB (EU, $0.74/hr) ·
**RTX PRO 4000 Blackwell** 24 GB (EU, $0.57/hr). The EU cards run pruned int8
only (bf16/int8 don't fit 24 GB usefully) with `--reserve-vram 2.5`; the RTX
4000 additionally needs `--cache-none --disable-pinned-memory` because its
container has a 29 GB RAM cap that otherwise OOM-kills weight staging.

## B200: weight-family comparison (warm, 20 steps)

The headline: **bf16 is the fastest family on the B200** — quantization costs
speed here (dequant overhead) and only pays when VRAM is scarce.

| Config                          | bf16        | int8        | pruned int8 | bf16 VRAM | int8 VRAM | pruned VRAM |
|---------------------------------|-------------|-------------|-------------|-----------|-----------|-------------|
| t2v 864×480 5 s                 | **51 s**    | 63 s        | 63 s        | 159 GB    | 63 GB     | 69 GB       |
| t2v 864×480 15 s                | **240 s**   | 270 s       | 270 s       | 182 GB    | 101 GB    | 75 GB       |
| t2v 1344×768 5 s                | **169 s**   | 197 s       | 194 s       | 177 GB    | 99 GB     | 73 GB       |
| ref2v 1 ref 864×480 5 s         | **54 s**    | 68 s        | ~68 s¹      | 186 GB    | 95 GB     | 68 GB       |
| ref2v 4 refs match              | **63 s**    | 86 s        | 81 s        | 154 GB    | 95 GB     | 70 GB       |
| ref2v 8 refs match              | **79 s**    | 99 s        | 95 s        | 185 GB    | 96 GB     | 68 GB       |
| ref2v 8 refs match 1344×768     | **282 s**   | 335 s       | 339 s       | 158 GB    | 103 GB    | 77 GB       |
| ref2v 4 refs max                | **91 s**    | 117 s       | 112 s       | 156 GB    | 96 GB     | 70 GB       |
| ref2v 8 refs max                | **785 s**   | 891 s       | 841 s       | 177 GB    | 112 GB    | 92 GB       |

¹ pruned 1-ref mean is 90±41 s over 3 runs because the first run absorbed a
model swap; the steady-state runs are ~68 s, matching int8.

bf16 peaks at 155–187 GB — it *only* runs on ≥180 GB cards. int8 fits under
112 GB (H200-class); pruned int8 under ~95 GB worst-case, ~70 GB typical
(H100-class without offload, 24 GB cards with offload).

## Scaling behavior (B200, pruned int8)

Duration (864×480 t2v): 5 s → 63 s, 10 s → 154 s, 15 s → 270 s. Cost per
output-second rises from 12.6→15.4→18.0 s — mildly superlinear (attention).

Resolution (5 s t2v): 608×352 → 32 s, 864×480 → 63 s, 1344×768 → 194 s.
Roughly ∝ pixels^1.2. The corner case 1344×768×15 s = **1108 s** (18.5 min).

Reference count (864×480 5 s, `match` sizing): 1 ref ≈ 68 s, 4 refs ≈ 81 s,
8 refs ≈ 95 s — ~+4 s per extra reference image. Cheap.

Reference sizing `max` is the expensive lever: with 8 large refs it goes
95 s → **841 s** (~9×), because 2048px reference tokens ride through every
sampling step. Use `match` unless identity fidelity demands otherwise.

## Cross-GPU (pruned int8, t2v 864×480 5 s, warm)

| GPU              | $/hr  | time/video      | videos/hr | $/video    | notes                          |
|------------------|-------|-----------------|-----------|------------|--------------------------------|
| B200 (bf16)      | $6.94 | 51 s            | 70        | $0.098     | fastest family on this card    |
| B200 (pruned)    | $6.94 | 63 s            | 57        | $0.121     |                                |
| RTX 4090         | $0.74 | ~222 s          | ~16       | ~$0.046    | suite still running — interim  |
| RTX PRO 4000     | $0.57 | pending         | pending   | pending    | suite still running            |

(EU numbers will be finalized when their suites complete; ref2v numbers for
both cards land then too.)

## Concurrency (2 ComfyUI instances, one B200)

See `docs/CONCURRENCY.md` for the model. Experiment: two resident instances,
sequential pair vs concurrent pair of identical warm t2v jobs.

RESULTS_PENDING

## Takeaways

1. **On the B200, run bf16.** Fastest and highest quality; quantized families
   exist for smaller cards, not for speed.
2. **Many references are cheap; big references are not.** 8 refs at `match`
   costs ~1.4× a single ref. `max` sizing costs up to ~9×. Default `match`.
3. **Duration and resolution both scale superlinearly**; the trained envelope
   corner (15 s @ 1344×768) costs ~18 min even on a B200 — treat full-res
   long clips as premium jobs.
4. **24 GB consumer cards work but pay a heavy offload tax** (~3.5× B200
   latency rather than the ~3× raw-compute ratio), and cheap pod tiers bring
   operational traps: container RAM caps (29 GB on the RTX 4000 pod) and
   stock torch builds too old for H3 (needs ≥2.5 for `enable_gqa`; Blackwell
   needs ≥2.7+cu128).