I've been using MiniMax H3 on my M5 Pro 64GB MacBook Pro through ComfyUI. It works extremely well.
I had to modify the default ComfyUI workflows to use a GGUF quant (city96's ComfyUI-GGUF custom node, UnetLoaderGGUF in place of the stock loader) [0].
I use the model labeled Q5_K_M. There is Q8_0 available as well, which is 34GB and fits fine in 64GB unified memory if you keep resolution modest.
The main issue is speed, a ~9-second 480x864 clip at 20 steps takes me a bit over an hour. So this will be cool to try for the speed up alone.
There's a lot of great information and workflows available to follow on the r/StableDiffusion subreddit.
GGUF is unsupported by ComfyUI’s memory management system that enables running models much larger than fit in VRAM with tolerable efficiency via weight streaming, but for unified memory systems that system is less relevant (unless using models too big to run in unified memory AND having fast enough mass storage to benefit from direct-from-disk weight streaming.)
So I did test this, and it doesn't work because the quantized layers need torch._int_mm, which PyTorch's MPS backend doesn't implement. It just throws NotImplementedError.
This implementation is much faster on my M5 Max, like a few minutes for the same video, but on an M5 Max with 128GB, didn't test on M5 Pro. About memory, could be executed on 64GB with a few changes.
My understanding is that that tends to be more critical with LLMs than image/video gen models, which are relatively more compute vs. memory transfer intensive than LLMs
Performance might still end up being bounded by data transfer speed if SSD streaming is heavily used to make up for limited RAM. By comparison, it doesn't take many parallel-batched sessions to make LLM decode compute-bound on typical hardware (hence seeing very limited gains from even wider batching), but this just doesn't apply when streaming weights from disk, the setting is completely different.
It's better than all private video models like Veo. Yes, I too am incredulous they released this open weights. It's a VERY disruptive model in all the good ways.
In the AMA Minimax said that H3 could support sparse attention, that would be a huge speedup! I wonder if there are any news on that. H3 is very cool. EDIT: testing a --sparse-attention optional mode based on what they said in the Reddit post.
> On my 128GB M4 Max Mac Studio, generating a 15s 480p video with MiniMax H3 in ComfyUI takes an hour and a half.
That's crazy, a RTX Pro 6000 does that in in 2-3 minutes (give or take, depending on your exact settings). LLMs don't make the difference between standalone GPU vs unified memory + CPU so obvious as diffusion models seems to do.
It’s always been the case, it’s more the anomaly that LLMs work at comparable speeds on M series because almost all other ML runs way faster on Nvidia cards.
Seriously, very dumb model compared to what you can run locally, but holy moly is it FAST on one GPU, seriously impressive. Can't wait for those to be scaled up a bit to fit perfectly within 96GB VRAM, then they'll be competitive.
> On the 128 GB M5 Max, clean end-to-end image+audio and embedded-video+audio renders completed in 74.58 and 76.99 seconds respectively, each with about a 40.1 GB peak physical footprint and zero swaps.
Looks like it uses 40GB? So your 96GB mac setup should work fine i guess (Model itself is 33B)
This repo looks neat, but I hope they add some more clear benchmarks because that time (74.58s) is pretty meaningless given that the it/s (and total time) is highly dependent on mode (T2V vs I2V vs REF2V), resolution (0.4, 0.6mp, etc), duration (5-15 seconds), etc.
It shouldn't? Unless you're using BF16 for all weights (I'm using NVFP4 for the text encoder, otherwise everything BF16 (and audio F32)) you'll fit it all within 96GB VRAM, bugs non-with-standing :) I've been fitting this within 96GB VRAM without issues.
> This misconstruction is very common, included in print publications spanning several centuries. It might be considered an alternative spelling, albeit still a mistaken usage.
Thanks though, I never actually knew so was helpful :)
you should have a look at https://github.com/deepbeepmeep/Wan2GP which is the goto tool for "gpu poor", although as people below already pointed out you should be fine with comfyui's standard setup aswell
First, I think they're not even talking about GPUs, this is macOS hardware so unified memory. Secondly, if they were talking about GPUs, then 96GB VRAM is hardly what people refer to when they say "gpu poor".
2) Since it's unified memory, you won't have 96GB available.
3) I offered a solution that is usually recommended to the "gpu poor", if he's concerned with how much memory he would need.
4) I stated, that people already pointed out how he should be fine and that "gpu poor" doesn't apply to him.
5) "gpu poor" depends on what model you are trying to use. If you want to run Kimi or GLM you are still "gpu poor" even if you have an RTX Pro 6000 with 96GB of VRAM.
The majority of the population doesn't even have access to a functioning computer. So yeah maybe somebody truly talented can figure their way out of that hole after a few years but that's where alot of people are starting from.
“I am, somehow, less interested in the weight and convolutions of Einstein's brain than in the near certainty that people of equal talent have lived and died in cotton fields and sweatshops."— Stephen Jay Gould
To show the world you're a "world class talent", whatever that means, also suggests you would either have to be a genius or have enough resources to work on your side quests. Latter implies you're well off so, no, I don't think there's zero correlation between the two in all cases.
Redis, hping and dump1090 were all side projects he started/written while having a full time job.
Your comment really sounds like "many other people would do A and B if they just had time and money to do so", but he's been doing so since time and money were major constraints.
I noticed on a bar TV the other day that some of the Chromecast screensaver landscape photo credits were to Peter Norvig. They were really lovely pictures.
I've run into Peter Norvig twice. Once at a YC event; the other when I parked my motorhome in front of his house in Palo Alto for a couple of days while visiting a friend who happened to live on the same street (not on purpose, I didn't know it was his house, it was just where I found sufficient open street parking for a huge motorhome, big houses with fewer cars on the street than on my friend's block). I ran into him while walking my dog, he asked about the motorhome and we talked travel. He was lovely both times. Not everyone is nice about a big motorhome parking on their block, especially in California, but he was friendly.
I'm looking to setup a way to create images for my own instagram marketing.
I do not care how long it takes to make 10 variations of a post as that speed would still be faster than me making it.
Does this model work with ComfyUI easily? Can I just download it?
This will be a little faster right now on an M4 or M5 because it's optimized for Apple Silicon. Assuming this model is still state of the art in six months, which might not be a surprise given how long other video models have taken, this should be much, much faster with the M7 chip.
While you will find plenty of people willing to scam you to pay for “adult entertainment workflows”, the built in templates in ComfyUI for the model—perhaps dropping in a Lora Loader node for the a Turbo lora for speed—handle running the model, the subject matter adaptation isn’t really a workflow issue but one of reference/control images/audio/videos and prompting.
For Minimax H3, more than most models, you should read (and, if you are using an LLM for prompt assistance, make it sure it has access to) the official prompt guidelines, as each of the main models (fl2va that handles text-to-video and first- and/or last-frame-to-video and r2va that handles more complex reference cases) has its own structured prompt format (with many common features).
H3 is quite uncensored, but was not trained on p0rn, so it has no anatomy clues needed to generate that kind of stuff. For softer adult content it is reported to be fine on Reddit.
Other than for pure t2v usage you probably don't need a LoRA for much, I have seen evidence that it is knowledgeable enough that it can handle a fair amount of anatomy looking and behaving reasonably with just relevant cues in control (for the fl2va model) or reference (for the r2va model) images.
And the r2va model can also use video input for motion reference.
> I’d personally steer clear of messaging platforms for this - who knows what one might stumble into there
Personally I have no interest, but sometime browse stuff out of curiosity. But this got more of my curiosity, what kind of "stuff" are you implying they might stumble upon on the open, public internet? Sure, some NSFW, horror and otherwise weird stuff is there, especially around AI generation, but hardly something that will leave you traumatized, unless I misunderstand what you're implying?
Avoiding use of “girl” when you aren’t asking for a feminine child is good advice for any model that isn't specifically trained on a specialized prompting vocabulary (e.g., danbooru tags) where “girl” has a different meaning.
But I haven't seen anything reliable about H3 being particularly special in ther regard.
are these still 2026 problems? a simple vector index would associate girl as a synonym for adult woman as is used by both men and women in the English lexicon
even a Chinese model that thinks its Claude when asked would have inherited this association
I had to modify the default ComfyUI workflows to use a GGUF quant (city96's ComfyUI-GGUF custom node, UnetLoaderGGUF in place of the stock loader) [0].
I use the model labeled Q5_K_M. There is Q8_0 available as well, which is 34GB and fits fine in 64GB unified memory if you keep resolution modest.
The main issue is speed, a ~9-second 480x864 clip at 20 steps takes me a bit over an hour. So this will be cool to try for the speed up alone.
There's a lot of great information and workflows available to follow on the r/StableDiffusion subreddit.
[0] https://huggingface.co/Abiray/MiniMax-H3-GGUF/tree/main/unet
that's rough. for comparison, i tried the exact same parameters on my 5090 RTX and it took 2 minutes to generate.
i believe diffusion models are primarily compute bound so the macs aren't really the ideal hardware for this kind of stuff
Put Codex to work on deploying it now, hoping the speed can improve quite a lot :-) Thanks anyway
That's crazy, a RTX Pro 6000 does that in in 2-3 minutes (give or take, depending on your exact settings). LLMs don't make the difference between standalone GPU vs unified memory + CPU so obvious as diffusion models seems to do.
Which codex?
Seriously, very dumb model compared to what you can run locally, but holy moly is it FAST on one GPU, seriously impressive. Can't wait for those to be scaled up a bit to fit perfectly within 96GB VRAM, then they'll be competitive.
Given money to 2 different companies for access to this one (Hailou, Kling).
LLMs were never a market for me, video was at first, but this may be all I need moving forward.
I almost never see anyone talk about Vidu such as their Q3 series - the one I have paid most for and use most.
> On the 128 GB M5 Max, clean end-to-end image+audio and embedded-video+audio renders completed in 74.58 and 76.99 seconds respectively, each with about a 40.1 GB peak physical footprint and zero swaps.
Looks like it uses 40GB? So your 96GB mac setup should work fine i guess (Model itself is 33B)
Anyway, good input!
> This misconstruction is very common, included in print publications spanning several centuries. It might be considered an alternative spelling, albeit still a mistaken usage.
Thanks though, I never actually knew so was helpful :)
2) Since it's unified memory, you won't have 96GB available.
3) I offered a solution that is usually recommended to the "gpu poor", if he's concerned with how much memory he would need.
4) I stated, that people already pointed out how he should be fine and that "gpu poor" doesn't apply to him.
5) "gpu poor" depends on what model you are trying to use. If you want to run Kimi or GLM you are still "gpu poor" even if you have an RTX Pro 6000 with 96GB of VRAM.
Your comment really sounds like "many other people would do A and B if they just had time and money to do so", but he's been doing so since time and money were major constraints.
I noticed on a bar TV the other day that some of the Chromecast screensaver landscape photo credits were to Peter Norvig. They were really lovely pictures.
Does this model work with ComfyUI easily? Can I just download it?
What are some adult entertainment workflows in comfyui, I need best loras, best prompts to start with
and the communities, are they on telegram or something?
For Minimax H3, more than most models, you should read (and, if you are using an LLM for prompt assistance, make it sure it has access to) the official prompt guidelines, as each of the main models (fl2va that handles text-to-video and first- and/or last-frame-to-video and r2va that handles more complex reference cases) has its own structured prompt format (with many common features).
And the r2va model can also use video input for motion reference.
I’d personally steer clear of messaging platforms for this - who knows what one might stumble into there
Personally I have no interest, but sometime browse stuff out of curiosity. But this got more of my curiosity, what kind of "stuff" are you implying they might stumble upon on the open, public internet? Sure, some NSFW, horror and otherwise weird stuff is there, especially around AI generation, but hardly something that will leave you traumatized, unless I misunderstand what you're implying?
But I haven't seen anything reliable about H3 being particularly special in ther regard.
even a Chinese model that thinks its Claude when asked would have inherited this association
And no, including the word "girl" in H3 does not lead to CSAM in any way, shape or form, but it's a great example how FUD quickly spreads.