7 Ways to Use the Trending MiniMax H3 Right Now (No GPU or Colab Required)
On August 3, 2026, the world of AI video generation was turned upside down.
The Chinese company MiniMax released their video generation model, "MiniMax H3," as "open weights" for free on Hugging Face.
What are open weights?
Since this might be an unfamiliar term, I will explain it first.
The core of an AI model is a massive data file called "weights." Think of it as the brain of the AI after it has finished learning. Open weights refers to releasing this core model file in a format that anyone can download.
Services like ChatGPT only let you "use the AI over a server," and you never get the actual model. With open weights, you get the model itself, so you can run it directly on your own PC or in the cloud. In other words:
・You don't have to pay API usage fees.
・There are no limits on the number of generations.
・You are not bound by the service provider's restrictions or filters.
This is the destructive power of "open weights."
To give you an idea of how big of a deal this is:
・It can generate videos up to 2K resolution and 15 seconds long.
・It generates video and stereo audio "simultaneously" (dialogue, sound effects, and background music are included from the start).
・It ranks 3rd in the world on benchmarks for creating video with audio from images (1st is Seedance 2.0, 2nd is Gemini Omni Flash).
・And you can run it on your own PC or in the cloud. With zero API usage fees.
There was even news that immediately after the announcement, the stock price of MiniMax's parent company jumped more than 10% on the Hong Kong market. It was an announcement at a level that moves the market.
However, looking at X, while there is a lot of excitement, there are also many voices of confusion. People don't know if their GPU specs are sufficient. They don't have a clear idea of how to run it in the cloud. They tried to install it but got stuck with errors.
I understand. With open-weight models, "free to use" and "easy to use" are completely different things.
In this article, I have compiled "every pattern" for using MiniMax H3. From local PCs, Google Colab, and GPU clouds to official APIs, third-party APIs, and no-code web services. I have structured this so that you will definitely be able to choose one that fits your environment and budget.
This is a long article, but feel free to read only the sections that suit you from the table of contents.
Chapter 1: What is MiniMax H3?
First, let's organize the basic information.
MiniMax H3 is a video generation model announced on July 31, 2026, and released as open weights on Hugging Face on August 3 (China time). It is also sometimes referred to as Hailuo 3.0 or Hailuo 03.
To summarize the specs:
• Output is 5–15 seconds, 24fps, up to 2K resolution
• Simultaneously generates 32kHz stereo audio through the same inference path as the video
• Omni-modal design that allows mixed input of text, images, video, and audio
• Can accept up to 12 reference materials in total, including images, videos, and audio
• Handles everything in a single model: Text-to-Video (T2V), first/last frame specification (FL2VA), generation from reference materials (Ref2VA, R2V), in-place editing to replace parts of existing footage, and motion transfer to extract camera work or editing rhythm from a reference video and apply it to other material
The "simultaneous audio generation" is particularly revolutionary.
Conventional video generation AI typically creates the video first and then adds sound in a separate process. Since H3 creates the video and audio together in the same generation process, the timing of lip movements and dialogue, as well as movements and sound effects, is perfectly synced from the start. This eliminates the entire post-production process of syncing audio.
If you specify "door sound here" or "BGM from here" in the prompt, the sound will be inserted at exactly that timing. It is truly impressive when you try it out.
Chapter 2: What changes with open weights?
As explained at the beginning, open weights mean you get the model itself. Here is how that affects actual operation:
• If you run it on your own GPU, the generation cost is just the electricity bill
• No limit on the number of generations
• You are not affected by service congestion or changes in regulations
• No usage-based fees even when integrated into automated pipelines
The fourth point is especially significant. The fact that there are no API costs is a life-or-death issue for those involved in mass production or automation. API charges of several dozen to several hundred yen per video can easily result in a difference of tens of thousands to hundreds of thousands of yen when you are creating 100 or 1,000 videos.
However, there are some points to note. Many articles skip this, so I will explain it in detail.
Chapter 3: License Considerations (Important)
H3 is released under the proprietary MiniMax H3 Community License. Here are the key points.
1. Commercial use is permitted, but license attribution is required.
2. Companies with annual revenue exceeding $20 million (approx. 3.1 billion yen) require prior written consent from MiniMax.
3. The EU, UK, South Korea, and the US are outside the license's coverage area. Use in these regions requires separate consultation with MiniMax.
4. Using generated content to train other AI models is prohibited.
5. Publishing content without clearly stating it is AI-generated is prohibited.
The interesting part is point 3. Developers in Los Angeles cannot download it, but developers in Tokyo can. Since Japan is not an excluded region, we can use it normally. We have entered an era where geopolitical circumstances are reflected in AI model licenses.
Also, regarding the 'uncensored' aspect: it is true that when running it locally, there are no content filters like those found in API services. However, as mentioned above, there are prohibitions under the license, and the publication of generated content is naturally subject to the terms and laws of each platform. Freedom and lawlessness are different things, so please use it responsibly.
Chapter 4: Overview of How to Use It
Now for the main topic. There are broadly seven ways to use H3.
Method 1: Run on a local PC (ComfyUI)
Method 2: Run on Google Colab
Method 3: Rent a GPU cloud (e.g., RunPod)
Method 4: Use the official MiniMax API
Method 5: Use via third-party APIs (e.g., fal, RunComfy)
Method 6: Use via ComfyUI API nodes or Comfy Cloud
Method 7: Use via no-code web services (e.g., Hailuo app)
Roughly speaking, methods 1-3 are in the world of 'running the weights yourself = almost zero generation cost,' while 4-7 are in the world of 'using hosted services = easy but pay-as-you-go'.
We will look at them in order.
Chapter 5: Method 1: Running it on a local PC
This is the most exciting method, but also the most challenging. However, it has been reported since its release that H3 runs on lower specs than expected, which is the center of the current buzz.
Recommended environment requirements
・ComfyUI 0.30.0 or higher (This is the most important. Older versions will not recognize the H3 nodes)
・VRAM 12GB or more (16GB or more for comfortable use; 24GB class is most commonly reported in verification)
・Main memory (RAM) 64GB or more recommended. This is because the system works by offloading what doesn't fit in VRAM to RAM
・Disk space: Approximately 40GB (approx. 60GB if using R2V as well)
・NVIDIA GPU. CUDA assumes a newer stack (cu130 series); there are reports that it will be significantly slower if it is older
Why it runs on 12GB
The full-precision (BF16) model in the original repository is 288GB in total, which is a size intended for multi-GPU servers. It is not a size that can be handled on a personal PC.
Therefore, the ComfyUI official team (Comfy-Org) prepared a quantized version. By replacing the modulation layers, which account for about 40% of the model, with lookup tables and combining int8 quantization with custom kernels, they reduced the minimum memory usage by 66%, from 123.6GB to 42.5GB. Furthermore, by using dynamic VRAM offloading, they have brought it to a point where it can even run on RTX 3060 class GPUs.
If you are running it locally, this Comfy-Org version is the only choice. Consider the original diffusers format to be for advanced users who build their own workflows.
Actual performance reports
・Reports of a 5-second video taking just over 6 minutes and a 10-second video taking about 16 minutes on an RTX 4070 (12GB VRAM)
・Reports of a 5-second video taking about 70 seconds and a 15-second full-length video with audio taking about 10 minutes on an RTX 4090 (24GB VRAM) with optimized settings
・However, a 12GB VRAM environment is quite tight, and the configuration is essentially based on having 64GB or more of RAM
There are also limitations
・Locally, you can basically only generate up to 768p (768px on the short side)
・The upscaling part responsible for 2K output (H3-Regenerate-2K) does not have its weights released and is only provided via the official hosted API
In short, a hybrid approach—running locally at 768p and using the cloud only for upscaling key shots to 2K—is the most realistic solution.
Overview of Installation Steps
1. Update ComfyUI to version 0.30.0 or higher (updating the desktop app may leave the core outdated, so it is safer to pull the latest core via git clone).
2. Open the official T2V, I2V, and R2V workflows from the ComfyUI template library.
3. Follow the instructions within the workflow to download the diffusion model, text encoder, video VAE, and audio VAE files, and place them in the specified folders.
4. First, generate a test video at a lower resolution and shorter duration (960x544, about 5 seconds) to verify it works.
5. Once it works, increase the resolution and duration.
Common pitfalls include ComfyUI version mismatches, CUDA version issues, and forgetting to select the audio VAE (resulting in no sound). The golden rule is to run the official workflow as-is without adding custom optimization nodes at first.
Who this is for
・People who already own a gaming PC (RTX 3060 12GB or better).
・People who generate a large volume of content and want to keep running costs near zero.
・People who enjoy troubleshooting environment setup issues.
Regarding this local installation, I am planning to publish a separate article with a complete, step-by-step guide based on my own setup experience, including the points where I got stuck. Since this summary alone will almost certainly lead to issues for some, it is safer for those planning to install it to wait for that guide.
Chapter 6: Method 2 - Running on Google Colab
This is the perfect solution for those who say, "I don't have a GPU, but I can afford a little over 1,000 yen a month."
Reports have emerged of people running the H3 local model on Google Colab's L4 GPU (24GB VRAM), with cost estimates shared at around 0.23 Euros—less than 40 Japanese yen—per 15-second video (approx. 720p).
To put this in perspective, creating the same 15-second video via the official API (768p) costs $1.20, or about 180 yen. That is 4 to 5 times the cost of Colab. In some cases, it is over 8 times cheaper than APIs for similar models from other companies.
Advantages of Colab
・Zero initial investment. Everything is done entirely in the browser.
・Available via Pro subscription (monthly billing) or by purchasing compute units as needed.
- If it breaks, just recreate it. No need to worry about environmental pollution.
Disadvantages
- Requires knowledge to set up the notebook (many people give up here).
- When the session disconnects, the model often needs to be re-downloaded (it's 40GB, so it's actually quite painful; you need to find workarounds like caching to Drive).
- Not suitable for long-term continuous operation or automation.
For those unfamiliar with notebooks, there is honestly still a barrier. However, in terms of cost-performance alone, it is currently one of the best options available.
Chapter 7: Method 3 Rent a GPU Cloud (RunPod, etc.)
Instead of buying a graphics card, you rent one by the hour. RunPod and Vast.ai are the standard choices.
There was an interesting calculation on X. If you rent an RTX 5090 on RunPod for about 160 yen per hour, and it takes 10 minutes to generate a 15-second video, you can make 6 videos per hour. That's about 27 yen per video. The calculation shows it's cheaper than the official API. Plus, your room doesn't get hot (this is a serious issue in the summer).
RunPod also released an official guide for H3, explaining that you can get it running just by setting up a pod with their official ComfyUI template and downloading the model via the Hugging Face CLI. The scale is such that it's safer to secure a 600GB volume disk if you want to try all quantization patterns.
Advantages
- You can use GPUs that individuals can't buy, such as the RTX 4090, 5090, or A100, for a few hundred yen per hour.
- You are only charged for the time you use. Zero yen for months you don't use it.
- Environments can be reproduced using templates, so you can recreate them even if you break them.
Disadvantages
- There is a setup process every time, such as starting the pod, placing the model, and updating ComfyUI.
- If you want to maintain storage, there is a storage fee.
- Since it's pay-as-you-go, if you 'fall asleep with it running,' your wallet will suffer.
Here is a gap I am personally targeting. Even though GPU clouds have the best cost-performance, 90% of people drop out because of the hassle of the setup. So, I should just package all that setup. I will write about this in detail in the final chapter.
Chapter 8: Method 4 Use the Official MiniMax API
A method of calling the model hosted by MiniMax via API without running the weights yourself.
・The API has been available since the initial announcement, and documentation is already public.
・Information shared suggests that the estimated cost is around $1.20 for a 15-second 768p video.
・The biggest advantage is that this (along with official partner services) is currently the only place where 2K output (H3-Regenerate-2K) can be used.
Local and Colab environments are limited to 768p. If you have a project that requires 2K as the final deliverable, you have no choice but to go through the official API (or a service that can call it).
Who this is for
・Developers who do not want to set up any environment at all
・People who require 2K resolution
・People who generate a small number of videos and are not bothered by pay-as-you-go billing
Chapter 9: Method 5 - Using via Third-Party APIs
In addition to the official one, there are already several providers hosting H3 and offering it via API.
fal.ai
A major overseas generative AI infrastructure provider. Various H3 functions, including text-to-video, image-to-video (specifying the first frame, or a pair of first and last frames), generation from reference materials, and video editing, are available as serverless APIs. It supports 2K output and 7 aspect ratios. You can pass up to 9 images, 3 videos, and 3 audio files as references per generation. This is a strong candidate if developers want to integrate it into their products.
RunComfy
A cloud ComfyUI service that also provides an H3 API. It offers a unified parameter format and sample code, using a method where you send a request as an asynchronous job and retrieve the result using a request_id.
The common point among these is that a fee is added to the official output rate. In exchange for convenience, the unit price per video is higher than hosting it yourself.
Chapter 10: Method 6 - ComfyUI API Nodes, Comfy Cloud
Good news for ComfyUI users: H3 supported ComfyUI from the first day of its release. Moreover, there are two ways to use it.
The first is the local open-weight version explained so far.
The second is Partner Nodes (API nodes). When you place an H3 node on a ComfyUI workflow, the generation process itself is executed on the MiniMax server side. No model download or local GPU is required, and your Comfy API account is charged based on the number of seconds generated. The template library includes three types of sample workflows: text-to-video, first/last frame, and generation from reference.
You can use it in the same way with Comfy Cloud (ComfyUI running in a browser), allowing you to keep your ComfyUI workflow assets intact while simply running them in the cloud.
The ability to switch between local 768p generation and API node 2K generation within the same workflow culture is a strength of the ComfyUI ecosystem.
Chapter 11: Method 7 - Using No-Code Web Services
For those who say, 'Code and ComfyUI are too much. I just want a video to come out when I press a button.'
Hailuo (Official MiniMax App)
Since H3 is also Hailuo 3.0, using the official Hailuo app or web service is the easiest no-code route.
Third-party web services that have integrated H3, such as Morphic
Some offer free plans, so you can start trying it out without payment. A common structure is to use a paid plan for commercial use or to increase generation volume.
The official ComfyUI browser execution (see previous chapter) is also effectively as easy as this.
The disadvantages are that the cost per generation is the highest, and you are limited to the filters and terms of service of the platform. It is the perfect entry point for 'making your first video and being amazed.'
Chapter 12: Cost Comparison Summary
Here is a summary of the cost per 15-second video based on current reports (please treat this as a guide, as it changes based on exchange rates and conditions).
Local PC (Own GPU)
Cost per video: Electricity only (a few yen)
Initial investment: GPU cost (from 40,000 to 50,000 yen for a used RTX 3060 12GB)
Resolution: Up to 768p
Google Colab (L4)
Cost per video: Just under 40 yen (reported at around 0.23 euros)
Initial investment: Zero
Resolution: Around 720p
GPU Cloud (RunPod RTX 5090)
Approx. 27 yen per video (160 yen/hour, based on 6 videos per hour)
Zero initial investment
Resolution up to 768p (fast depending on GPU)
Official API
Approx. 180 yen per video (reported at $1.20 for 768p)
Zero initial investment
Supports 2K resolution
Third-party API (e.g., fal)
Approx. cost per video is the official rate plus a service fee
Zero initial investment
Supports 2K resolution
No-code web services
Cost per video depends on the plan (free tiers available)
Zero initial investment
Resolution depends on the service
The numbers make it clear: if you want to mass-produce, you have three choices (local, Colab, or GPU cloud); if you need one-offs or 2K, use an API; and if you just want to try it out, use a web service.
Chapter 13: Which one should you choose in the end?
Here is the conclusion based on your needs.
For those who just want to make one video
→ Method 7 (Web services like Hailuo). You can have a video ready in 5 minutes.
For those with a gaming PC equipped with an RTX 3060 or better
→ Method 1 (Local ComfyUI). If you have 32GB of RAM, consider upgrading to 64GB. Welcome to a world where you can generate as much as you want for just the cost of electricity.
For those with a weak PC but who can afford a few thousand yen a month
→ Method 2 (Colab) or Method 3 (GPU cloud). These two currently offer the best cost-performance.
For developers who want to integrate it into apps or services
→ Method 4 (Official API) or Method 5 (fal, etc.). Especially if you need 2K resolution.
For those already using ComfyUI
→ A combination of Method 1 and Method 6. Use 768p locally for everyday tasks, and use API nodes for 2K resolution on key shots.
Chapter 14: The 'Final Barrier' That Remains, and What I Decided to Build
After reading this far, many of you must be thinking the following:
'I understand the options. But updating ComfyUI, downloading and placing four model files, matching CUDA versions, and configuring cloud pods. That's exactly what I can't do.'
That is true. H3 has reached the point where it 'runs on a 12GB GPU.' But it hasn't reached the point where 'anyone can run it.' The reality is that the benefits of open weights are only reaching those who can handle the environment setup.
That is why I decided to build a system myself that skips the entire environment setup process.
The form I am aiming for is as follows:
- Press a button to launch a GPU environment in the cloud, which starts up with the entire H3 generation environment already set up.
- Even with zero knowledge of ComfyUI, you can enter a prompt and get a video with audio.
- Just shut it down when you're done. You only pay for the hourly GPU rental cost, with no API usage fees.
- In other words, even if you combine it with mass production or automation, costs will not grow linearly.
I will eliminate the barrier of specs and the barrier of environment setup through the system itself. I make my living by developing web services on my own, so this is my home turf. I will be sharing the development process on this note as well. Please follow me if you are interested.
Summary
・MiniMax H3 is a video model capable of 2K resolution, 15-second clips, and simultaneous audio generation, with open weights released on August 3, 2026.
・Japan is within the licensed region. Commercial use is also permitted (subject to conditions such as attribution requirements).
・Quantized versions have been reported to run on GPUs with 12GB of VRAM, making it accessible for personal PCs.
・There are 7 ways to use it: local, Colab, GPU cloud, official API, third-party API, ComfyUI API nodes, and web services.
・Use self-hosting for mass production, API-based services for 2K resolution, and web services for testing.
・I have decided to build my own system that completely skips the environment setup process. I plan to publish the development process on note as well.
・Next time, I plan to explain the installation procedure for local ComfyUI in detail in a practical report format.
↓I verified it. What was the result???
[Verification] Can the trending MiniMax H3 run on a regular PC with 8GB of VRAM? The results of pushing an RTX 3050 to its limits
The democratization of video generation is finally here. Don't miss out on this wave.
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