4-bit plus DiffSynth: an 8GB-VRAM route — MiniMax H3 tutorial
Run H3 on smaller GPUs through 4-bit quantization and VRAM-adaptive inference.
Original: ModelScope @ModelScope2022
ModelScope @ModelScope2022 · Source
Run your MiniMax-H3 locally, even on Mac!
With the 4-bit-quantized version coupled with DiffSynth-Studio, you can run MiniMax-H3 with a minimum of 8GB vRAM 🚀🚀🚀
🤖 https://modelscope.ai/models/DiffSynth-Studio/MiniMax-H3-NF4
More to explore:
• Inference that adapt to your available vRAM: takes as little as 8GB
• Runs in extreme mode on a 16GB Apple M-series Mac
• LoRA training with a 24GB consumer GPU
All while preserving the powerful capability of MiniMax-H3! Let’s cook 🔥
Audience: Users with 8GB VRAM or those testing a quantized H3 path on Mac.
Hardware: The source reports an 8GB minimum; verify actual speed and supported modes.
Prerequisites
- DiffSynth-Studio
- Matching 4-bit H3 weights
- Enough system memory and storage
Steps
- Create an isolated Python environment and install DiffSynth-Studio from source. Confirm the current H3 documentation still reports an approximately 7GB NF4 minimum.
- Open model_inference_low_vram/MiniMax-H3-NF4-FL2VA.py and preserve the four matching NF4 origin_file_pattern values.
- Keep the documented vram_limit strategy of available VRAM minus 2GB; never spoof a limit larger than physical memory.
- Start at 480×832 and 124 frames or the example's smaller setting, hold the seed constant, and allow CPU/GPU parameter movement to finish.
- Use ffprobe to confirm t2va.mp4 contains video, 24fps, and a 32kHz stereo audio stream, then listen for sync.
- After the low-VRAM baseline passes, test Ref2VA, canvas, or frame count separately. Record total time and system memory instead of reporting GPU VRAM alone.
Commands
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio && pip install -e .
python examples/minimax_h3/model_inference_low_vram/MiniMax-H3-NF4-FL2VA.py
ffprobe -v error -show_streams t2va.mp4
Caveats
- Running in low VRAM does not imply speed, and quantized quality differs from native weights.
View original source · ModelScope · 2026-08-23
Guide · Source checked: 2026-08-23 · Site testing: No generation test performed
Check the prerequisites, then follow the author for the full demonstration and updates.
Follow the original source for its materials. Missing assets or settings are not reconstructed here.
Applicable versions
DiffSynth-Studio main @ 2026-08-23 · MiniMax-H3-NF4 low-VRAM example
Troubleshooting
An NF4 component fails to download or load
Check each of the four origin_file_pattern values and model IDs; do not mix FL2VA and Ref2VA components.
VRAM fits but the system stalls
Low-VRAM quantization relies on CPU offload; inspect system RAM and swap, then reduce canvas or frames.