minimax-h3-nvfp4-lambda-modality

MiniMax-H3 NVFP4 — what the smoothing scale should be calibrated on

Five end-to-end 50-step generations, one BF16 reference, same prompt and same seed (1101), all on one RTX 5090. They differ in one thing: which rows of the packed [text | video | audio] sequence were used to derive the per-input-channel smoothing scale lambda.

The question they answer: H3 runs full self-attention over a packed sequence of three modalities, but a linear layer has one weight, so W * lambda is shared by all three. Per-channel activation profiles differ per modality — measured correlations between them are near zero or negative — so a single lambda cannot be right for all of them. Is favouring video worth it?

Answer: no. lambda calibrated on video alone is the worst of the four smoothed configs end-to-end. Calibrating on all rows is the right default.

The runs

grid.mp4 is all of them on one timeline, captioned.

Read the numbers with care

RESULTS.md has the table. One column in it is easy to misread, so it is worth saying here:

Mean RGB is not a quality metric between two working quantizations. Every run shares BF16's seed and therefore its initial noise. A faithful quantization stays in the same sample; one that perturbs the trajectory enough lands in a different — and perfectly plausible — sample. Run 4 is a coherent video of a different scene, not a broken one, and its large mean-RGB delta is reporting the scene change, not damage. corr (frame-aligned against BF16) is the column that separates "same scene, degraded" from "different scene".

Mean RGB was the right instrument earlier, when the failure being chased was a genuinely washed-out output from a misapplied smooth_factor permutation. It stopped being the right instrument the moment every candidate started producing a real video.

The metric that picks the winner also hides the cost

The calibration's own error column scores each candidate lambda on rows drawn uniformly from the packed sequence — which is what deepcompressor's OutputsError objective specifies. Uniform means proportional, and video is 98.6% of the rows. So that column ranks the runs like this (relative L2 vs bf16, median over 312 layers, rank-32 branch included):

lambda=video wins that metric and loses end to end. The metric is not wrong; it is answering a question about the 98.6%, and the damage is in the other 1.4%.

Re-scoring with the same quantizer on rows sampled per modality (scripts/calib_sample_modal.py + scripts/score_modal_lambda.py, 4096 rows per modality per layer, 312 layers) shows what the proportional column could not:

Change against no smoothing (negative = helps):

lambda=all is the only column negative in all three rows. lambda=video buys video 4.3 % and charges text 27.9 % and audio 11.5 % for it. lambda=text is free but pointless — it does nothing for video.

The worst layers make the trade obvious. lambda=video vs lambda=all, text error:

A 12.7x text error for a 1 % video gain, in one layer.

Why

NVFP4 puts 16 consecutive input channels under one FP8 scale set by that group's absmax, so a channel whose own absmax is far below its group's loses log2(group_absmax / channel_absmax) bits, and lambda reshapes exactly that profile because the kernel sees X / lambda. The per-modality channel profiles are uncorrelated to anti-correlated (b0.to_q videotext −0.066; b25.to_v videoaudio −0.390), and dividing by a vector uncorrelated with your own profile sharpens it rather than flattening it. The statistics-only proxy (scripts/diag_lambda_crossmodal.py, bits lost, no GPU) agrees with the measurement above: video −0.437 bits, text +0.216, audio +0.243 under lambda=video.

Text is ~1.4% of the rows, but under full self-attention every video row attends to it. Degrading the conditioning degrades the video conditioned on it — which is how the per-layer video error can fall while the generated video gets worse.

Calibration

Same as minimax-h3-svdquant-calib: 128 video prompts, 768x1344, 124 frames, 50 steps, rank 32, 39-candidate lambda grid, 100 iterations of low-rank refit against OutputsError. The only thing varied across runs 2–5 is which modality mask the activation statistics were accumulated under (scripts/calib_stats_modal.py), and hence which absmax feeds the lambda grid.

Run 1 is lambda = 1 with the low-rank branch zeroed — the floor, plain W4A4. Run 2 is lambda = 1 with the rank-32 branch refit against the raw weight — it isolates the low-rank contribution from the smoothing contribution.

Base model: MiniMaxAI/MiniMax-H3 @ bfc8ed0353f5a9733be73e6b2c98ec0948195b86.

One thing a consumer must not get wrong

SVDQW4A4Linear.smooth_factor is addressed by the kernel in MMA-interleaved channel order, while every other tensor picks that interleave up from NunchakuWeightPacker. Writing lambda in natural order gives 12 of every 16 channels another channel's lambda. Details and the permutation are in the minimax-h3-svdquant-calib README. Everything here was produced with that fix applied.

Note the interaction with this page: a sharper lambda makes that bug worse, so before the fix, lambda=video looked catastrophic and lambda=text looked fine — for a reason that had nothing to do with modality.