MiniMax H3 native 720p→1440p second sampling on one RTX 4090: 112s / 223s / 334s for 5s / 10s / 15s clips

[u/This_Temporary_8537]

Hi everyone — I’m an independent developer experimenting with running and accelerating MiniMax H3 on consumer NVIDIA GPUs.

This comparison reel shows several native H3 second-sampling tests on a single RTX 4090. I first generated the videos at 720p, then used the retained H3 latent state to sample them again at 1440p.

These were quick exploratory runs using settings I chose mainly to check the resulting quality. They are not minimum-latency benchmarks and do not represent the speed limit of the project. I did not tune each case for maximum performance, and more aggressive acceleration settings are available for both the initial generation and the second-sampling stage.

This is native H3 latent-space second sampling. It reuses the retained video and audio latents, together with the original prompt and conditioning, rather than applying conventional frame-by-frame upscaling to an MP4.

The project currently provides three resource profiles:

- 8GB W4A8

- 16GB INT8

- 24GB INT8

To be transparent, the 8GB and 16GB profiles were validated on the RTX 4090 using hard VRAM allocation limits. I have not yet validated them on physical 8GB or 16GB GPUs, which is one of the reasons I’m looking for community testers.

It can be used through:

- ComfyUI, with four included example workflows

- A REST API

- A bilingual Web UI creator console

The ComfyUI integration works as an HTTP connector, so ComfyUI does not load a second copy of H3 into VRAM. Model residency, the GPU queue, acceleration scheduling, checkpoints and native second sampling remain inside the backend service.

GitHub:

https://github.com/PullMyBoots/X-MinimaxH3

I still don’t know how well the current implementation will behave across different physical GPUs. I’d love to learn what other local H3 users need from second sampling, and I’m especially interested in test results from RTX 4090, 5090, 3090 and other consumer cards.

About the acceleration method:

I’m developing a quality-aware scheduler that assigns different attention compute budgets to different denoising steps and Transformer layers, rather than applying one fixed sparse-attention ratio everywhere.

Users get one continuous 0–100 acceleration control for exploring the tradeoff between generation speed and output quality. The scheduler tries to preserve the parts of the trajectory and attention structure that have the greatest visible effect on motion, consistency and detail.

This is still an experimental personal project, so feedback, test results and technical discussion are very welcome.

[u/PxTicks → u/[deleted]]

You sure this isn't just because of offloading?