[Verification] Can the popular MiniMax H3 run on a standard 8GB VRAM PC? The results of challenging the limits of an RTX 3050
In my previous article, I introduced 7 ways to use the popular video generation AI, "MiniMax H3".
This time, it's the practical application. I decided to be the guinea pig and see what happens if you try to run H3—which is said to "run on 12GB VRAM"—on a machine with even less than that.
Here are the specs of the test machine.
CPU: Core i7-12700KF
RAM: 32GB
GPU: RTX 3050 (8GB VRAM)
Windows 11 Pro
This is a common configuration for a gaming PC. The recommended requirements for H3 are 12GB or more of VRAM and 64GB or more of RAM. In other words, both VRAM and RAM are insufficient. This verification is for those who are wondering, "My PC is about this level, will it run?"
I'll give you the conclusion first: It ran. It even completed a video with music in HD resolution. However, the limits appear in a different way. More on that in the second half.
Wall #1: Even the ComfyUI I installed a month ago is "outdated"
First, I launched my existing ComfyUI (Windows Portable version) and checked the version.
ComfyUI version: 0.27.0
To run H3, you need ComfyUI 0.30.0 or higher.
This environment was actually just updated in July 2026.
Even so, it's too old for H3. In this world, if you stay away for a month, you become like Urashima Taro.
Furthermore, two unsettling warnings appeared in the startup log.
The first was a warning that "pytorch with cu130 or higher" is required.
The high-speed version of H3's quantization processing assumes the CUDA 13 generation, and if you stay on an older version, it will result in slow fallback behavior.
The second was a fatal blow: "DynamicVRAM support requires Pytorch version 2.8 or later." Dynamic VRAM offloading, which is the key to running H3 on low VRAM, cannot be used unless you have PyTorch 2.8 or later.
This environment is on 2.6. Trying to run it on 8GB VRAM without offloading is over before it even begins.
In other words, I was in a state where a three-stage update of the ComfyUI core, PyTorch, and CUDA was necessary.
Wall #2: The update bat file fails "silently"
The Portable version comes with an update script called update_comfyui.bat. When I ran this, it appeared to succeed at first glance, but it actually failed.
Looking closely at the logs, even though it downloaded the new version (e.g., frontend 1.48.6) and uninstalled the old one, the final "Successfully installed" message listed the old version number (e.g., 1.45.20).
It's contradictory.
In the middle, there was also a warning saying "Failed to remove contents in a temporary directory." The update of the embedded Python environment ended up in a half-baked state.
If it had stopped with an error, I would have noticed, but it pretends to have finished normally. This is the worst kind of failure.
I think people who have been using the Portable version for a long time are highly likely to fall into the same trap.
I changed my strategy here. It's faster to do a fresh install than to try and repair the old environment.
A Turning Point: The latest desktop version was too good
ComfyUI now has an official desktop app with an installer called "Comfy Desktop." When I tried a fresh install of this, things took an unexpected turn.
When you choose between Cloud or Local during installation (I chose Local, which is free to use) and specify the installation path, a "Choose a starter workflow" screen appears during the initial setup. There,
MiniMax H3 is selected from the start as "Recommended." It's about 53GB in size. Just by checking the box and pressing Install, the installer handles everything from downloading the model to placing it.
In the installation steps I had researched beforehand, this was the part most prone to errors: "Download the 4 model files (diffusion model, text encoder, video VAE, audio VAE) yourself and place each in the correct folder." That entire process has vanished.
When I checked the version after installation,
ComfyUI 0.30.2, PyTorch 2.12.1+cu130. The three walls that stood in my way with the Portable version were all gone with a single fresh install.
As of August 2026, introducing H3 has become as simple as "just following the installer's instructions."
I had intended to write an explanatory article on manual placement, but that is no longer necessary.
That means, now that the hurdle of installation has disappeared, only one wall remains: machine specs.
Time for a generation test
Once the setup is complete, the H3 text-to-video workflow opens.
The configuration is simple: a selector to decide the resolution, a main node containing the prompt and various settings, and a save node.
The template comes pre-loaded with an English sample prompt that specifies an entire action movie escape scene, using a format where you specify cuts by timecode and instruct the audio (wind, footsteps, ambient noise, and even the timing of the music's climax) in text.
Since the goal this time is a sanity check rather than quality, I used the prompt as it was in the sample.
The first attempt was the minimum configuration. I set the resolution selector to 0.2 megapixels (608x352) and the length to 2 seconds, then executed it.
The 8GB VRAM survived the model loading and the start of sampling, which I had assumed were the most likely points of failure. And about 4 minutes later, a video appeared.
What surprised me when I played it back was the sound. The video has music overlaid.
The audio instructions in the prompt work, and a video with background music is generated in one go.
This is H3's simultaneous audio generation. And this is without ever opening editing software.
From here, I increased the conditions. Here are the results.
List of generation times (5-second video, 24fps, measured queue values)
608x352 (0.2MP) 2 seconds: 232 seconds (approx. 4 minutes)
https://x.com/yukiGPTs/status/2085270548716953797
608x352 (0.2MP) 5 seconds: 416 seconds (approx. 7 minutes)
https://x.com/yukiGPTs/status/2085270700731023756
864x480 (0.4MP) 5 seconds: 886 seconds (approx. 15 minutes)
https://x.com/yukiGPTs/status/2085270835741491645
1280x736 (0.9MP) 5 seconds: 3082 seconds (approx. 51 minutes)
https://x.com/yukiGPTs/status/2085270959586770978
Everything finished successfully. OOM (out-of-memory crash) never occurred once.
Looking at the numbers, an interesting trend emerges.
Even when extending the length from 2 seconds to 5 seconds, the time only increases by about 1.8 times.
However, when increasing the resolution, the generation time jumps: 2 times from 0.2MP to 0.4MP, and 3.5 times from 0.4MP to 0.9MP.
H3's processing cost is more strongly governed by resolution than by length.
To be honest, I ran the final 1280x736 test under quite poor conditions. I couldn't close my browser, so at the start, memory usage was at 93%, with Chrome alone consuming about 8GB.
Even so, it didn't crash. It finished in 51 minutes by offloading what didn't fit in VRAM to RAM, and what didn't fit in RAM to the SSD page file. ComfyUI's dynamic VRAM offloading is quite excellent.
Conclusion: "It doesn't crash." The limit comes in the form of generation time.
MiniMax H3 ran on a machine below the recommended specs, with an RTX 3050, 8GB VRAM, and 32GB RAM. It even produced HD resolution (1280x736) videos with music.
However, the limit appears as time, not as a crash. For social media drafts or prompt experiments, 608x352 at 7 minutes is sufficient, but 1280x736 takes 51 minutes per video.
Even if you work all day, you can only make about 10 videos. There is still a gap between "it runs" and "it can be mass-produced."
Still, the money spent on this verification was 0 yen. No API fees, no subscriptions; the only cost was electricity.
Cutting-edge video generation AI that only a few people could touch three days ago is now outputting videos with music for free on an 8GB graphics card. We live in an incredible era.
So, what should you do if you want to mass-produce high-resolution videos at a practical speed? Before buying a new graphics card, there is a method worth trying.
It's a way to rent top-class GPUs for a few hundred yen per hour.
Next time, I'll cover the RunPod introduction. Just like this time, I will write it based on actual verification from scratch.
Furthermore, I have decided to develop a system myself that eliminates the need for this kind of environment setup.
The RunPod verification is also the first step toward that development.
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