nynxz/MiniMax-H3-Experiments

MiniMax-H3 experiments

Measured experiments on making MiniMax-H3 (video + audio) smaller and faster on consumer Blackwell GPUs, with files that load in stock ComfyUI. Quantization uses NVIDIA Model Optimizer; every number comes from a results file and is regenerated by one script.

Work in progress. Results are final for what they cover; new experiments are added as they finish.

Contents

Page	What it covers
Calibrated NVFP4 checkpoint	12.7 GB NVFP4 transformer, recipe sweep, comparison with published NVFP4 files, RTX 5090 speed, comparison videos
Attention config LoRAs	per-layer kitchen INT8 / SOL sparse attention allocations that beat the reference config LoRA
AdaLN analysis	why 13B of the 33B parameters reduce to a rank-12 timestep basis ("pruned" checkpoints)
Methods	how quality is measured (one-step error), noise floor, simulation vs ComfyUI kernels

Headline results (RTX 5090, 960×544, 5 s clip)

Setup	Transformer	s/step (20 steps)	Video / audio error
ComfyUI repack, INT8 ConvRot	21.0 GB	3.87	–
This repo: calibrated NVFP4	12.7 GB	3.53	15.4% / 15.3%
… + attention config LoRA	12.7 GB	1.76
lilcheaty pruned_nvfp4 (uncalibrated, same layout)	12.5 GB	–	18.2% / 16.8%
rockerBOO pruned_nvfp4 (FP8 attention, BF16 edges)	20.1 GB	4.14 (ConvRot variant)	12.7% / 9.1%

Errors are one-step output error against BF16 on held-out prompts (see Methods); the attention LoRA's own error is measured separately on the attention page.

Key findings

• 13 of H3's 33.1B transformer parameters are AdaLN projections that depend only on the timestep and factor exactly into a rank-12 basis (66 → ~40 GB in BF16) at any step count or shift. ComfyUI's pruned build does the same at rank 8; at the output the two are indistinguishable.
• 4-bit weights, not 4-bit activations, carry most of the NVFP4 error; calibration (local Hessian) cuts the error of a naive cast by ~15% at the same size and speed. Block-wise scale learning overfit and did not help.
• On an RTX 5090 attention is ~73% of each step, so NVFP4 alone is ~9% faster than INT8; with an attention config LoRA it is ~2× faster per step than plain INT8.
• Our attention allocations beat the reference config LoRA at the same speed: ~18% less video error and ~15% less audio error on the 4-step stack they were tuned on, and still better on the 20-step base stack.

Files

File	What	Size
diffusion_models/minimax_h3_fl2va_pruned_nvfp4_calibrated.safetensors	FL2VA / T2VA transformer, all block GEMMs NVFP4 (local-Hessian), AdaLN curve form rank 12	12.7 GB
loras/minimax_h3_attn_balanced.safetensors	attention config LoRA: reference speed, less error	~10 KB
loras/minimax_h3_attn_quality.safetensors	attention config LoRA: ~4% slower, clearly less error	~10 KB
loras/minimax_h3_attn_fast_dmad4step.safetensors	attention config LoRA for the dmad 4-step stack	~10 KB
loras/sinks/*.safetensors	experimental attention LoRAs with audio/text sinks; need the ComfyUI PR in Attention	~10 KB

Checkpoint: put it in models/diffusion_models/ and pick it in the H3 template's Load Diffusion Model node; keep the usual H3 text encoder and VAEs. "Pruned" means the 13B AdaLN parameters are replaced by an equivalent timestep table plus rank-12 projections; nothing else is removed. NVFP4 runs natively on Blackwell (RTX 50-series, B200); other GPUs load it but dequantize. Weight LoRAs (e.g. 4-step distillation) are merged by ComfyUI by re-quantizing with fresh scales, which discards the calibration; the bypass LoRA loader keeps it at ~15% speed cost.

Attention LoRAs: load with Load LoRA (Model Only) at strength 1.0, after any weight LoRA, with attention override nodes disabled. They hold no weights.

License and attribution

MiniMax H3 is licensed under the MiniMax H3 Community License Agreement, Copyright © 2026 MiniMax. All Rights Reserved. These files are derivatives of MiniMax-H3 and are distributed under the same agreement, including its Acceptable Use Policy and territorial restrictions; see LICENSE.MiniMax-H3, NOTICE and the license. The H3 text encoder is Qwen3-VL-32B (Apache 2.0). Not affiliated with or endorsed by MiniMax or NVIDIA.