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MiniMax-H3 on 8x MI300X

This 1-Click image serves MiniMax-H3, a diffusion model that generates a video clip and its matching audio track together in a single pass, on one 8x AMD Instinct MI300X node. Inference runs on SGLang in diffusion mode across all 8 GPUs with 8-way sequence parallelism and the aiter attention backend tuned for gfx942.

Both partitions are baked in

The image carries 403 GB of weights under /opt/models/MiniMax-H3, including both released partitions:

• FL2VA for text-to-video with audio
• Ref2VA for reference-image-to-video with audio

There is no first-boot download and no runtime dependency on Hugging Face being reachable. The build fails loudly if either partition is missing, so a published image always carries the full payload.

A browser studio, not just an API

Boot the droplet and you get an AMD-branded web studio: type a prompt, press generate, watch progress, and download the finished MP4. No client libraries and no notebook required. The studio listens on loopback only and is reached with a one line SSH port-forward printed in the login banner, so it is never exposed to the internet by default. An OpenAI-style HTTP API is available on the same host for programmatic use.

The gfx942 correctness fix

On gfx942 the default scaled-dot-product-attention backend produces a repeating lattice artifact in the decoded video. The failure is silent: the job reports success and the MP4 contains valid H.264 and AAC streams, so mechanical checks all pass while the picture is corrupted. This image pins MINIMAX_H3_TORCH_SDPA_BACKEND=math, which produces correct output, and the studio’s health endpoint reports the pin so you can confirm it is in force.

Secured on first boot

The studio is protected by an API key generated uniquely on that droplet at first boot. No key is baked into the image, so nothing secret is published. The inference server itself binds to loopback and is never exposed directly.

8x only

The snapshot carries a large min_disk because of the baked weights, so DigitalOcean will only restore it onto the 8x MI300X SKU. That is also the only configuration with enough VRAM to shard this model.

Software Included

Deploying this Offering using the Control Panel

Click the Deploy to DigitalOcean button to deploy this offering. If you aren’t logged in, this link will prompt you to log in with your DigitalOcean account.

[](https://cloud.digitalocean.com/gpus/new?appId=3e77cfb6475958b0b04fdcb8&image=MiniMax-H3 Video+Audio on 8x MI300X FL2VA+Ref2VA on Ubuntu 24.04&type=applications)

Getting Started After Deploying MiniMax-H3 Video+Audio on 8x MI300X

After the droplet boots, SSH in as root. The login banner (MOTD) is the primary interface: it reports whether the model has finished loading, prints the exact command to open the studio, and prints the API key generated for this specific droplet. Nothing is pre-shared, the key is created on first boot.

The studio binds to loopback and is never exposed to the internet, so reach it with an SSH port-forward from your own machine:

ssh -N -L 8080:127.0.0.1:8080 root@YOUR_DROPLET_IP

Then open http://127.0.0.1:8080, paste the API key once when prompted, type a prompt and press Generate. A 6 second clip takes roughly 4 to 5 minutes on 8x MI300X. The finished video plays in the page and can be downloaded directly.

Prompting tip: H3 takes audio direction from the prompt text. Append an overall_soundscape: clause describing what should be heard, for example a wide shot of a desert at sunrise. overall_soundscape: wind and distant birds. Without it you still get an audio track, but it is unguided.

To use the HTTP API instead, POST to http://localhost:30010/v1/videos with a JSON body containing model, prompt and duration, then poll /v1/videos/<id> until status is completed.

The server loads the FL2VA (text-to-video) partition by default. To serve Ref2VA (reference-image-to-video) instead, set H3_PARTITION=Ref2VA via systemctl edit h3-server and restart the service. Useful commands: systemctl status h3-server, journalctl -fu h3-server, curl -s localhost:8080/api/health, amd-smi monitor.