---
title: MiniMax H3 Ultra
emoji: ⚡
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 6.20.0
app_file: app.py
pinned: true
short_description: Blackwell-native NVFP4 video + synchronized audio generation
suggested_hardware: zero-a10g
---

# MiniMax-H3 Ultra — pruned NVFP4 on Blackwell

Joint video and synchronized sound from MiniMax-H3, with the repeatedly executed transformer rebuilt around the
Blackwell-native ComfyUI optimization path:

- 12.5 GB pruned NVFP4 transformer instead of the 61.7 GiB BF16 inference transformer.
- 20.1B effective inference parameters instead of 33.1B; the redundant 13.04B AdaLN projection weights become a
  compact sampled timestep curve.
- One fused QKV projection per attention layer.
- One fused in-place Q/K RMSNorm + partial split-half RoPE kernel.
- Native CUDA 13 NVFP4 tensor-core GEMMs through `comfy-kitchen`.
- Segment-wise in-place AdaLN modulation and gated residual accumulation.
- Video and audio output heads run only on their own rows, not the full packed sequence.
- Prompt refinement and the rotary table are cached for the request instead of recomputed at every denoising step.
- The video VAE and audio VAE remain full precision.

The source model is [`MiniMaxAI/MiniMax-H3`](https://huggingface.co/MiniMaxAI/MiniMax-H3). The pruned NVFP4
checkpoint is
[`lilcheaty/MiniMax-H3-NVFP4`](https://huggingface.co/lilcheaty/MiniMax-H3-NVFP4), derived from ComfyUI's
[`MiniMax-H3`](https://huggingface.co/Comfy-Org/MiniMax-H3) repackage.

## Why the pruned transformer matters

MiniMax-H3's published model card notes that about 13B parameters live in AdaLN-related branches and that their
outputs can be precomputed for inference. The pruned checkpoint makes that concrete: it samples the shared timestep
embedding curve at 1025 points and linearly interpolates an 8-value coordinate for each requested timestep. Every
block's modulation projection consequently becomes `[96768, 8]` instead of `[96768, 2688]`.

| parameter group | original BF16 architecture | pruned architecture |
|---|---:|---:|
| AdaLN projections | 13.04B | 0.04B |
| MLP | 12.02B | 11.56B |
| attention | 8.02B | 7.71B |
| refiner, norms and embeddings | 0.05B | 0.80B |
| **total** | **33.12B** | **20.11B** |

The four large matrices in each of the 50 blocks—fused QKV, attention output, MLP up/gate and MLP down—are NVFP4.
The modulation curve, norms, embeddings, biases and final heads stay at higher precision.

## Why this is faster than the old 4-bit Space

Quantization alone does not guarantee speed. The older 4-bit comparison paid for CPU offload traffic on every layer
because its runtime did not keep the transformer resident. This engine is about 12 GB, so the transformer, both VAEs,
working activations and decoder workspace fit together on the 95 GiB `xlarge` ZeroGPU worker. There is no layerwise
host-device weight traffic in the denoising loop.

ComfyUI reports about a 2× NVFP4 uplift over FP8/BF16 on Blackwell in supported workloads. The H3 checkpoint author
measured 1.90 s/iteration for pruned NVFP4 versus 2.17 s/iteration for pruned INT8 ConvRot on an RTX PRO 6000
Blackwell at 864×480, 39 frames. Those numbers are useful implementation evidence, not a promise for every canvas:
H3 attention grows quadratically with packed sequence length, so resolution, duration and keyframe vision tokens
still dominate large requests.

## Split deployment

The full checkpoint cannot fit under a single Space's 150 GB storage ceiling. This Space remains the denoising half:

| component | where it runs | precision / format |
|---|---|---|
| Qwen3-VL layer-50 conditioner | [`qwen3vl-conditioner`](https://huggingface.co/spaces/multimodalart/qwen3vl-conditioner) | BF16 |
| H3 transformer | this Space | pruned NVFP4 + higher-precision islands |
| video VAE | this Space | full precision checkpoint policy |
| audio VAE | this Space | FP32 |

The wire format is `prompt_embeds` plus `text_token_tags` in a safetensors file. The conditioner also returns the
resolved canvas, aligned frame count and prompt plan. Keyframes are encoded again by this Space's video VAE so the
conditioning latents exactly match the pixels seen by the conditioner.

## Kernel path

`h3_nvfp4.py` adapts the public ComfyUI H3 implementation to diffusers' packed transformer signature. It deliberately
does not install or launch the ComfyUI application. The small adapter uses only `comfy-kitchen` for:

1. Dynamic NVFP4 activation quantization and native FP4 matrix multiplication.
2. Fused in-place Q/K RMSNorm and three-axis split-half rotary embedding.

Attention itself stays on diffusers' `_native_cudnn` backend, which is faster than the default SDPA path on the
ZeroGPU RTX PRO 6000 pool. The MLP uses one fused QKV-style gate/up matrix, in-place SiLU×up, and the NVFP4 down
projection.

The old BF16/AoTI engine remains available with `H3_ENGINE=bf16`. It is useful as a quality/debug reference, but it is
not the default.

## Quality trade-off

NVFP4 is approximate. The checkpoint author reports that 4-bit weights can show more mid-motion artifacts and weaker
shape retention than the larger INT8 ConvRot checkpoint on difficult 15-second clips. The comparison was not fully
controlled, so treat it as a real caution rather than a quantified quality score.

The Space keeps both VAEs full precision and leaves AdaLN, norms, embeddings and output heads out of NVFP4. For the
exact original denoiser, set `H3_ENGINE=bf16`; this restores the 61.7 GiB unquantized transformer and its AoTI option.

## Space variables

| variable | default | meaning |
|---|---|---|
| `H3_ENGINE` | `nvfp4` | `nvfp4` ultra engine or `bf16` reference engine. |
| `H3_NVFP4_REPO` | `lilcheaty/MiniMax-H3-NVFP4` | Repository containing the pruned Comfy-format transformer. |
| `H3_NVFP4_FILE` | `minimax_h3_fl2va_pruned_nvfp4.safetensors` | FL2VA/T2VA transformer file. |
| `H3_MODEL_REPO` | `MiniMaxAI/MiniMax-H3` | Canonical schedulers and VAE checkpoint. |
| `H3_CONDITIONER` | `multimodalart/qwen3vl-conditioner` | Remote layer-50 conditioner Space. |
| `H3_PLACEMENT` | `lazy` (`nvfp4`) | Move the compact transformer and VAEs on the first GPU call, then keep them resident. |
| `H3_ATTENTION` | `_native_cudnn` | Attention backend for both the main stack and text refiner. |
| `H3_GPU_SIZE` | `xlarge` | 95 GiB Blackwell ZeroGPU allocation. |
| `H3_AOTI` | `0` | BF16 engine only: load the optional repeated-block AoTI package. |

## Runtime requirements

- PyTorch 2.11 with CUDA 13.0.
- A Blackwell GPU (`sm120` for this Space). NVFP4 on older architectures is emulated and can be slower than BF16.
- `comfy-kitchen==0.2.26` for the native layouts and fused Q/K kernel.
- The pinned MiniMax-H3 diffusers pull request for the modular schedulers, packing and VAE decode path.

No secret is required. All model artifacts are public, and `gradio_client` forwards the requesting user's ZeroGPU
identity to the conditioner.

## Attribution

The fused/pruned model structure follows
[`comfy/ldm/minimax/model.py`](https://github.com/Comfy-Org/ComfyUI/blob/master/comfy/ldm/minimax/model.py) from
ComfyUI (Apache-2.0). The quantized checkpoint and its conversion notes are from
[`lilcheaty/MiniMax-H3-NVFP4`](https://huggingface.co/lilcheaty/MiniMax-H3-NVFP4). MiniMax-H3 weights remain governed
by the MiniMax-H3 Community License Agreement.