Open source
MiniMax H3 is open source — can you self-host?
MiniMax released the H3 weights, and community projects like VDN-H3 push inference speed dramatically. Here's the reality check for anyone tempted to skip the API.
What's actually available
- MiniMax-H3 (official): open weights on Hugging Face, ~72 GB, supports up to 2K and 15 seconds with native audio.
- VDN-H3: community hybrid-attention rework that replaces long-range attention with a linear branch, plus boundary anchors for consistency.
- H3 Max / H3 Max Turbo: fal's post-trained variants — closed source, API only, near real-time.
The 11-second benchmark, decoded
VDN-H3's headline is 11.23 seconds to denoise a ~14.4-second clip on 8× NVIDIA B200 GPUs — about 1.28× realtime. But that number covers denoising only. Prompt encoding (a Qwen3-VL-32B call), VAE decode, and MP4 encoding all add wall-clock time, and the authors recommend splitting them onto separate machines.
| Setup | Compute | ~14s clip denoise |
|---|---|---|
| Dense H3 | 1× B200 | 13.95 min |
| VDN-H3 FP8 | 1× B200 | ~5.3 min |
| VDN-H3 FP8 distributed | 8× B200 | ~1.2 min |
| VDN-H3 + DMD distillation | 8× B200 | 11.23 s |
So: self-host or use the API?
For most builders, the answer is still the API. A single HGX/DGX B200 node with 8 GPUs and 1.44 TB aggregate memory costs far more per month than paying H3 Max Turbo at the API rate ($0.025/s at 480p, $0.04/s at 768p after promo). The open-source path becomes compelling in two scenarios:
- Heavy always-on use (48+ hours of generation a day) on your own GPU fleet.
- Model-release risk: once H3 weights are broadly deployed, prices trend down, and self-hosting becomes a floor on provider pricing.
Our approach: generate through fal today, publish transparency pages like this one, and keep the open-source hardware math on file for the day your volume justifies a GPU node.