Mixed W4A8 checkpoints for MiniMax H3 Ref2VA and FL2VA, converted from the pruned BF16 ComfyUI checkpoints.
[!IMPORTANT]
These checkpoints require the custom loader and comfy-kitchen W4A8 build from starsFriday/ComfyUI-W4A8-Loader. Do not use ComfyUI's standard Load Diffusion Model node; it does not register the experimental asym_w4a8_int8 format.
Files
File
Variant
Size
minimax_h3_ref2va_pruned_w4a8_mixed.safetensors
Ref2VA reference image, video, and audio workflows
12.54 GB
minimax_h3_fl2va_pruned_w4a8_mixed.safetensors
FL2VA text/image and first/last-frame workflows
12.54 GB
Installation
Stop ComfyUI, activate the Python environment used to run it, then install the node pack and native W4A8 implementation:
advanced/loaders → Load Experimental W4A8 Diffusion Model
In an existing MiniMax H3 workflow, replace only the standard diffusion-model loader with this node. Keep the remaining VAE, text encoder, conditioning, sampler, and output connections unchanged.
Requirements
Linux and an NVIDIA GPU with compute capability SM 80 or newer
Python 3.10 or newer
A CUDA-enabled PyTorch build using CUDA 12.8 or newer
CUDA Toolkit 12.8 or newer, including nvcc, to build comfy-kitchen
A recent ComfyUI version; tested with ComfyUI 0.30.0, Python 3.12.9, PyTorch 2.9.1+cu130, CUDA Toolkit 13.1, and RTX 4090
Windows, AMD/ROCm, Apple Silicon, and CPU-only execution are not supported by the provided installer.
Quantization
For each of the 50 MiniMax H3 transformer blocks, the converter quantizes these four weights:
attn.qkv_proj.weight
attn.out_proj.weight
mlp.fc1.weight
mlp.fc2.weight
The 200 targeted weights use packed 4-bit ConvRot storage, group size 16, ConvRot group size 256, a calibration-free 16-value codebook, and FP8 E4M3 scales. The optimized CUDA path quantizes activations to INT8 at runtime and uses INT8 GEMM. Non-targeted tensors remain in their source dtype, so these are mixed-precision checkpoints.
Each pruned BF16 source checkpoint is 40.23 GB; its mixed W4A8 result is 12.54 GB, a 68.8% file-size reduction. W4A8 is lossy, and file-size reduction does not guarantee an equal reduction in peak VRAM or workflow runtime.
This is an independent experimental conversion, not an official MiniMaxAI or ComfyUI release. Use of the weights remains subject to the MiniMax H3 Community License Agreement.
