Antirez/h3.c: MiniMax H3 inference engine for Mac computers
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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…
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GGUF is outdated in the latest versions of Comfy-UI. If you want a good balance of size, speed and quality you should use the int8_convrot model from the official Comfy Org Repo
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> a ~9-second 480x864 clip at 20 steps takes me a bit over an hourthat'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
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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.
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On my 128GB M4 Max Mac Studio, generating a 15s 480p video with MiniMax H3 in ComfyUI takes an hour and a half.Put Codex to work on deploying it now, hoping the speed can improve quite a lot :-) Thanks anyway
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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.
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This is where the DGX spark makes up a bit of the ground it loses on llm work, diffusion and cuda go together like peanut butter and jelly.
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Native inference optimization for Apple Silicon is such a game-changer for local-first workflows. Incredible performance work.
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This still requires 128Gb of memory, right? Me and my lowly 96Gb, like a commoner; missing out on the fun.