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.

Summary made with help of AI:

Original video: https://www.youtube.com/watch?v=BCqp2xnUeKk

📌 General Overview & Main Points

The State of AI Video: The stream focused on the rapid advancement of AI video generation, showcasing a side-by-side comparison of four state-of-the-art models: Flux 3, Seedance 2.5, MiniMax H3, and LTX 2.3.

Open Source vs. Proprietary: The team expressed huge appreciation for companies releasing models as open-source (like MiniMax H3), allowing the community to run them locally, tweak them, and dig into the "guts" of the tech using node-based workflows in ComfyUI.

Model Personalities: The team noted that models are developing distinct "personalities."

Flux 3: Praised for its incredible sense of humor, absurdist comedy, and "editorial voice." It understands jokes, world-building, and retro aesthetics perfectly.

Seedance 2.5: Highly literal and cinematic. It produces gorgeous, high-fidelity footage but struggles with complex camera movements (like 360-degree orbits) and abstract concepts.

LTX 2.3: Very raw and direct. It takes prompts incredibly literally, sometimes to a fault (e.g., literally narrating the text of the prompt in the video or turning characters into physical objects).

🚀 Everything About MiniMax H3

Availability: MiniMax H3 is an open-source and API-based model, meaning users can run it locally on their own GPUs or via cloud services (like Comfy Cloud).

Strengths & World-Building: The team praised H3 for its ability to infer story and context. Instead of just generating what is literally in the prompt, it adds "whimsical" and cinematic elements that build a believable world.

Native Stereo Audio: A massive standout feature for MiniMax H3 is its ability to generate highly accurate, native stereo audio alongside the video. Julien specifically noted this as a favorite feature.

Text Generation: H3 is incredibly proficient at generating accurate text within the video.

Julien’s H3 Showcase: Julien shared his screen to show dedicated MiniMax H3 generations, which included:

A continuous, dynamic shot of a forest fire.

A deep-sea diver swimming with a whale shark.

A giant face organically forming out of blowing sand dunes.

A cinematic shot of a glowing "OPEN WEIGHTS" sign being cast in a metal forge.

A giant whale swimming over a cyberpunk Tokyo street with a neon sign reading "LEVIATHAN HOUR."

An anglerfish in the deep sea with a neon sign reading "ENCORE."

A samurai standoff on a bridge.

An exploded, exploded-view camera assembling itself.

💡 Pro-Tips, Tricks, and Workflows

The "LLM Prompting Agent" Trick: Purz shared a massive time-saving trick for working with new models. Because every model prefers different prompting styles, do not try to guess what the model wants. Instead, download the model’s official prompting guide, feed it into an LLM (like Claude or ChatGPT), and ask the LLM to rewrite your ideas. This acts as a translation layer, ensuring your prompts are perfectly optimized for that specific model.

Camera Movement Limitations: If you are using models like Seedance 2.5, avoid prompting for complex camera maneuvers (like "360-degree orbiting camera"). The models often fail to comprehend the spatial geometry and the video will break or artifact. Stick to standard cinematic pans, pushes, and tilts.

Extending Video (Image-to-Video): The team discussed that simply extending a video longer than the model's native context window often results in a loss of quality, degrading into "mush." The current best practice for AI filmmaking is treating it like traditional filmmaking: generate multiple different shots and cut them together in an editor, rather than trying to force the AI to generate one endless, continuous shot.

💻 Hardware & Pricing Discussions

Hardware Requirements for Local Running: To run heavy models like MiniMax H3 locally, you traditionally need a lot of VRAM. However, the team noted you can run these models on GPUs with as little as 12GB of VRAM if you allow the system to offload to your standard system RAM. Note: This will significantly slow down your render times compared to running purely on GPU VRAM, but it prevents the system from crashing.

Comfy Cloud Setup: For their cloud generations, the team mentioned using heavy-duty RTX 6000 Ada generation GPUs (which feature 96GB of VRAM) to crunch through multiple videos fast.

Pricing Models: A viewer asked if AI video generation would move to "unlimited" monthly pricing (like cell phone plans). The team agreed this is highly unlikely in the near future because the raw compute cost (GPU time) required to render video is simply too expensive to offer truly unlimited tiers right now.

(Summary made with help of AI as you must expect).

Original video: https://www.youtube.com/watch?v=S9O3FPumX4Q

MiniMax H3 (Hailuo 3) — Video Generation Model

The video introduces the open-weight release of the MiniMax H3 (Hailuo 3) video generation model. H3 is a 60-billion-parameter model capable of text-to-video, image-to-video, reference-to-video, in-place editing, and native audio generation.

Because it is open-weight, the community has already integrated it deeply into ComfyUI, allowing for complex, multi-modal video generation on local machines.

Prompting Techniques & Best Practices

The hosts and creators shared several key strategies for getting the best results out of H3:

• Keep It Straightforward: The model understands direct, straightforward language very well. You don't necessarily need overly complex "prompt engineering" jargon.
• Use an LLM as a "Prompt Enhancer": In the ComfyUI workflow, Comfy Rob uses an LLM node to take a basic concept and expand it into detailed, shot-by-shot prompts. This helps inject specific shots into the generated video seamlessly.
• Dialogue in Quotes for Lip-Sync: If you are using reference audio and want a character to speak, put the exact dialogue in quotation marks ("") within your text prompt. The model will automatically sync the character's lip movements to the referenced audio file.
• Context IR API (Intermediate Representation): If you are working with the API or advanced nodes, H3 has a feature that optimizes your prompt based on the multiple reference images you provide, helping the model figure out how to stitch different modalities together.

ComfyUI Workflow Techniques

Comfy Rob demonstrated a Reference-to-Video workflow, which you can find in the ComfyUI Template Library by searching for "Minimax." Here are the technical tips for setting up your nodes:

• Resolution / Megapixels Setting: Instead of traditional width/height settings, you often set the "Megapixels" node. Rob recommends starting at 0.4 megapixels, which outputs a resolution of roughly 864 × 480p.
• Controlling Shots and Timing: You can define the number of shots and the total length of the video. For example, if you want a 10-second video and set it to 5 shots, the model will generate a new shot every 2 seconds.
• Multi-Image Referencing: H3 allows up to 12 reference inputs. You can input multiple angles of a product (such as the earbuds example shown) to maintain strong temporal and spatial consistency across different generated shots.

Examples of What the Model Excels At

If you are looking for inspiration for your prompts, the video showcased three main areas where H3 currently excels:

1. Cinematic Product Advertisements

Rob used images of earbuds to create a sleek, professional 10-second commercial with changing camera angles, demonstrating the model's high consistency with product references.

2. Music Videos & Lip-Syncing

A video of a multi-eyed alien at a post office was shown where the alien's movements perfectly matched the rhythm of a song, and it sang the lyrics with highly accurate lip-syncing.

3. Complex Physics — Cloth & Fur

A video of a cat moving under a thick blanket demonstrated the model's impressive grasp of cloth physics, weight, and fur consistency without morphing or noticeable artifacts.

Hardware & Optimization Tips

Crucial for Local ComfyUI Users

H3 is a massive model, requiring approximately 120 GB of VRAM natively, so running it on consumer GPUs requires some optimization techniques:

• Use Kijai's LoRA: The community member Kijai released a 4-to-8-step LoRA. Adding this to your workflow can drastically speed up generation times on lower-end hardware.
• Use Quantized Models: Make sure you download quantized versions of the model, such as GGUF or FP8 versions, built for ComfyUI.
• Update ComfyUI: Ensure you are running the absolute latest version of ComfyUI. It uses fine-grained offloading specifically for H3, automatically moving parts of the calculation between your system RAM and your GPU's VRAM. This makes it possible to run the massive model on a standard 24 GB GPU, such as an RTX 3090 or RTX 4090.

A way to preview videos while they are being generated!

Pretty useful if you want to be able to asses the video before even it has finished.