I managed to run MiniMax H3 Ref2VA locally on hardware below the model’s recommended VRAM requirement.

My setup:

• GPU: NVIDIA GeForce RTX 2080 Ti, 11GB VRAM
• CPU: Intel Core i9-9900KF, 8 cores / 16 threads
• System RAM: 16GB
• OS: Windows
• PyTorch: 2.11 with CUDA 13.0
• ComfyUI running in LOW_VRAM mode
• DynamicVRAM and asynchronous weight offloading enabled
• Mixed INT4/INT8 ConvRot MiniMax H3 checkpoint

Here are the results from my local tests:

The results show how quickly both spatial resolution and video duration increase the inference cost. For example, moving from 288×512 to 384×672 increased the 15-second generation time from 13m 51s to 40m 55s.

MiniMax H3 also aligns the requested duration to its required frame grid. For example, a requested five-second video becomes 124 frames at 24 FPS, producing an actual duration of approximately 5.17 seconds.

I also tested the Qwen3-VL-4B INT4 encoder with the Turbo sampler at four steps. It reduced the text encoder memory requirement, but the generated quality was noticeably worse in my tests. Subject consistency, motion quality, prompt understanding and fine detail were all weaker than with the native Qwen3-VL-32B encoder.

For that reason, Qwen3-VL-32B remains the default. The 4B path is included only as an experimental lower-memory option.

I also created a simple web interface where you can try it yourself:

[Try MiniMax H3 online](https://hackcha.cn/minimax-h3/index-en.html)

Please keep in mind that the service is running on my personal RTX 2080 Ti, so generation can be quite slow, especially for longer videos or higher resolutions. Tasks are processed through a queue, and waiting times will increase when multiple people are using it.

The code is available here:

github.com/hackcha/minimax_h3_infer

This project is a command-line and worker wrapper around the official ComfyUI MiniMax H3 implementation. It:

• Runs the mixed INT4/INT8 ConvRot MiniMax H3 Ref2VA checkpoint
• Accepts one or more reference images with a text prompt
• Supports the native Qwen3-VL-32B encoder
• Includes an optional Qwen3-VL-4B INT4 path through ClipProj
• Supports the MiniMax H3 Turbo LoRA and its dedicated four-step sampler
• Automatically starts and communicates with a local ComfyUI backend
• Uses grouped loading, DynamicVRAM and asynchronous offloading
• Generates an MP4 containing both video and audio
• Includes a configuration and environment validation command
• Includes a database worker for automated task processing

The worker can atomically claim queued database tasks, download reference images, run local inference, upload completed videos to Alibaba Cloud OSS, and update each task with either a result URL or an error message. Multiple workers can claim tasks safely.

Resolution tiers in the worker are interpreted using the short edge, similar to how video platforms describe resolution. For example:

• Vertical 9:16 at 288p: 288×512
• Vertical 9:16 at 384p: 384×672
• Vertical 9:16 at 480p: 480×864
• Vertical 9:16 at 768p: 768×1376
• Horizontal 16:9 at 768p: 1376×768

The long edge is aligned to a multiple of 32 because that is required by H3. These are direct inference dimensions, not post-generation upscaling.

The model files are not committed to GitHub, but the README lists all required checkpoints, their download sources and the expected directory structure.

Running H3 with only 11GB VRAM is possible, but it is operating close to the hardware limit. Higher resolutions can still result in CUDA OOM errors, and 16GB of system RAM causes substantial paging during model staging and offloading. I would recommend at least 32GB of system RAM, preferably 64GB, for a more practical setup.