MiniMax H3 recipes¶
Primary focus · Inference
MiniMax H3 recipes
Generate video and audio with H3. Run a server on CUDA or Apple Silicon MLX to iterate on prompts, or call the pipeline directly from Python.
7 maintained recipesCompare H3 modes and options
Supported modes
CUDA covers T2VA, FL2VA, and Ref2VA on the full checkpoint, plus FastH3 Preview, FastH3 LoRA, and a two-node FastH3 Preview recipe over Ray sequence parallel. MLX is T2VA only. Temporal --fast, spatial --fast-spatial, and opt-in VSA are flags on the same MLX script, not extra recipes.
| Mode | CUDA | MLX FastH3 |
|---|---|---|
| T2VA | Full H3, FastH3 Preview, FastH3 LoRA | FastH3 Preview after a local DiT conversion |
| FL2VA | Full H3 | Not wired |
| Ref2VA | Full H3 | Not wired |
Temporal --fast | No cookbook recipe | Shorter video denoise, MLX RIFE back to --num-frames, full-duration audio |
| VSA | Trained sparse attention on FastH3 CUDA | Opt-in. Convert with --include-vsa into a new directory such as ./FastH3-MLX-vsa, then pass --vsa. Do not overwrite an existing dense export. |
Spatial --fast-spatial | No cookbook recipe | Denoise and decode at height/width divided by --fast-spatial-scale, then resample. Composes with --fast. |
| Two-pass refine | No cookbook recipe | Not wired |
| 2-Spark SP | FastH3 Preview across two DGX Sparks with Ray sequence parallel (sp_size=2) over QSFP RoCE | Not wired |
Pick an H3 recipe and runtime
Choose the result you want, then use a maintained CUDA or MLX path. Device claims stay tied to checked-in sources and recorded runs.
Loading recipe details...
Exact device and memory details appear only when a recorded run supports them.
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- Model
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- Workload
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- Hardware
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- Expected output
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Loading...Running this script again starts a new process and reloads the model. To iterate in Python, create the generator once and reuse it for multiple prompts.
Start once, then change prompts in the playground or your app. You can run the server and clients on the same machine.
Prepare the machine
Run from your FastVideo clone in an activated Python environment. See installation requirements.
Download and convert MLX weights once
Skip this if the weights are already prepared. Edit the paths in examples/serving/mlx_fasth3.yaml to use your existing files. Install ffmpeg for video and audio output.
Start the server
Keep this terminal running while you use the playground or API clients.
Check that the server is ready
In another terminal, this returns {"status":"ok"} after startup.
Generate and download a video
Edit a prompt, generate, and watch the result. The playground uses the same server as cURL and your app.
Open after the server is ready. On a remote GPU machine, forward port 8000 to your computer first. This opens a local page, not a hosted demo.
Use cURL or an SDK
Each example submits a job, checks its status, and saves the MP4. The Python and JavaScript examples use OpenAI-compatible clients; no OpenAI account is needed.
This is a local development server without built-in API-key authentication. The client key local is a placeholder. Keep the server on loopback; use an authenticated TLS proxy before exposing it publicly. Run the JavaScript client in your webapp's backend, not in a browser with a private key.
Setup
The generated commands expect a local clone:
git clone https://github.com/hao-ai-lab/FastVideo.git
cd FastVideoUse Configuration for supported Python and CLI settings, Optimizations for attention and memory tradeoffs, and the support matrix for the supported model and optimization surface.
CUDA
UV_TORCH_BACKEND=cu130 uv pip install -e ".[fasth3]"Apple Silicon
uv pip install -e ".[mlx]"Follow the Apple Silicon guide for the download, conversion, and storage requirements.
Two DGX Sparks
uv pip install rayThe 2-Spark recipe needs two DGX Sparks on an active QSFP link, the same FastH3 snapshot on both NVMes, and a Ray cluster on top. Follow pairing two Sparks before running it.
Troubleshooting
- The full CUDA H3 examples request four GPUs by default. Their sources do not claim a GPU model or memory minimum.
- The FastH3 CUDA performance profile was measured on four GB200 GPUs. Use its strict profile when exact operation order matters more than the measured performance configuration.
- The MLX source runtime supports T2VA, optional temporal
--fast, optional spatial--fast-spatial, and opt-in VSA on--include-vsacheckpoints. FL2VA, Ref2VA, and two-pass refinement are not wired. - GPU count and VAE decode backend are configurable in the builder above for FastH3 recipes. Only the value shown by default has a recorded run; other supported values are unmeasured here.
- The 2-Spark recipe fixes its own GPU count and execution backend in its YAML config and is not affected by the GPU count knob above. It requires a two-node Ray cluster on the QSFP interconnect; see the setup step above.
- Gated or missing checkpoints: run
huggingface-cli loginand confirm you accepted the model's license on Hugging Face.
Evidence status
Every command, model ID, and flag on this page maps to a checked-in FastVideo source. Recipes marked Verified also have a recorded hardware path in linked FastVideo evidence. The full H3 CUDA examples remain Source-backed where the source records a GPU count but no GPU model or memory requirement. Unlisted hardware is unknown, not unsupported.