UCSD's FastVideo open-sources FastH3 Omni, a distilled MiniMax-H3 that makes reference-guided video with sound up to 12x faster and runs on an RTX 5090

Why it matters

【Key points】

The FastVideo team at UC San Diego's Hao AI Lab open-sourced FastH3 Omni on Oct 20, a distilled, faster version of MiniMax's open video model MiniMax-H3. Given a text prompt plus reference images, video clips and a voice sample, it generates video with synchronized sound. Weights, inference code and training code are all public; it had been an exclusive preview on Reactor's API.

1. Method: cuts the base model's 50 transformer evaluations to 8, using the team's Parallel Decoding Distillation (PDD) combined with Distribution Matching Distillation (DMD).

2. Speed (team's tests): 5.6-11.9x lower end-to-end latency across 27 matched workloads on eight GB200s, e.g. a 10-second clip from one reference image drops from ~78.5 s to ~9.4 s. The team says some tasks reach real-time 768p generation for 5, 10 and 15 s clips on its UniServe stack.

3. Local use: NVFP4 quantization-aware-distilled and FP8 weights for DGX Spark and RTX 5090, an RTX 4090 recipe for FP8, and day-0 ComfyUI support.

4. Training: 64 B300 GPUs, 3,500 steps, ~7,800 GPU-hours.

5. License: MiniMax-H3 Community License. Built with NVIDIA's FastGen team, Nuva Lab and Reactor.

【Verifiable】

Blog: https://haoailab.com/blogs/fastvideo-omniref-update/

Collection: https://huggingface.co/collections/FastVideo/fasth3-omni

Main weights and license: https://huggingface.co/FastVideo/FastVideo-FastH3-Omni-8-Step-V1

Code: https://github.com/hao-ai-lab/FastVideo

Announcement: https://x.com/haoailab/status/2112625528805208257

【Our read】

1. Reference-guided video with audio usually takes tens of seconds to minutes; getting it under ten seconds, and onto a single consumer GPU, makes open video models practical for individuals and small teams.

2. This is a third-party distillation, not a MiniMax release; quality comparisons are same-seed examples, and the team says they don't prove output parity.

3. All speed figures are the team's own and focus on new NVIDIA hardware.