--- title: GitHub - antirez/h3.c: MiniMax H3 inference engine for... description: h3-metal is a native inference engine for the MiniMax H3 video/audio generation model, optimized for Apple Silicon using Metal. It supports... canonical: https://daily.dev/posts/github---antirez-h3-c-minimax-h3-inference-engine-for-mac-computers-cpfhxfvjz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: GitHub - antirez/h3.c: MiniMax H3 inference engine for Mac computers | daily.dev og:description: h3-metal is a native inference engine for the MiniMax H3 video/audio generation model, optimized for Apple Silicon using Metal. It supports... og:url: https://daily.dev/posts/github---antirez-h3-c-minimax-h3-inference-engine-for-mac-computers-cpfhxfvjz og:image: https://api.daily.dev/og/posts/cpfHXFvjz.png og:image:alt: GitHub - antirez/h3.c: MiniMax H3 inference engine for Mac computers og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # GitHub - antirez/h3.c: MiniMax H3 inference engine for Mac computers **[Hacker News](https://daily.dev/sources/hn)** · 30 min read · 0 upvotes · 0 comments ## Summary h3-metal is a native inference engine for the MiniMax H3 video/audio generation model, optimized for Apple Silicon using Metal. It supports prompt-to-video/audio generation, first/last-frame conditioning, and ordered Ref2VA image/video/audio references. The project provides a detailed CLI with controls for denoising steps, layer thinning, token reduction, and internal canvas scaling to trade quality for speed. On M5 Max, a 4-step denoise takes ~3.5 seconds versus 26.4 seconds for a 29-pass reference. The implementation includes Metal 4/TensorOps BF16 and int8 paths, fused DiT kernels, activation memory aliasing, and streamed prompt encoding, achieving a 50-layer 19-transition 512x512 render in ~19 seconds with int8 QKV on M5 Max. Supports resolutions up to 1344x768 and durations up to ~15 seconds. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How fast is MiniMax H3 inference on M5 Max with int8 quantization at 512x512? A fixed 50-layer, 19-transition 512x512 render completes in approximately 19.32 seconds on M5 Max using the full int8 path (QKV, attention-output, and MLP quantized). The BF16 MPS baseline takes 36.30 seconds, int8 MLP alone takes 25.80 seconds, and adding int8 QKV brings it to 19.32 seconds. A 4-step denoise takes about 3.5 seconds versus 26.4 seconds for a 29-pass reference. _Engineers running H3 on Apple Silicon track these benchmark numbers on daily.dev as the optimization work progresses._ ### What are the valid frame counts and durations for MiniMax H3 video generation? H3 aligns frame requests upward to the pattern 5 + 17*n. Key values: 22 frames (0.917 s), 39 frames (1.625 s), 56 frames (2.333 s), 107 frames (4.458 s), 243 frames (10.125 s), 362 frames (15.083 s). Using --seconds 10 produces 243 frames (10.125 s). A request for 23 frames rounds up to 39. The intended workflow targets roughly 4–15 second videos. _Developers building H3-powered video pipelines find the latest generation constraints and workarounds on daily.dev._ ### What resolution limits apply to MiniMax H3 and which canvas sizes are validated? Width and height must each be multiples of 32, at least 32, and their product must not exceed 768×1344 pixels. Validated canvases include 512×512 (safest development size), 768×768 (close-quality square), 1344×768 and 768×1344 (landscape/portrait 768p limit), 1024×768 and 768×1024 (4:3 and 3:4), and 256×256 (fast native preview with automatic RoPE halving). Native 128×128 is unsupported. _Teams choosing canvas sizes for H3 video generation stay current on validated configurations through daily.dev._ ## Community take How the wider developer community reacted, aggregated from 1 discussion and 34 comments across hackernews (as of 2026-08-14). **TL;DR:** The community is largely impressed and excited by antirez's new native Metal inference engine for MiniMax H3, praising the significant speed improvements over ComfyUI-based workflows, while some note the need for clearer benchmarks and discuss hardware requirements. **Sentiment:** 70% positive · 25% mixed · 5% skeptical **The case for** - The engine delivers dramatic speed improvements over existing ComfyUI workflows — what took over an hour now takes a few minutes on comparable hardware. - The ~40GB peak memory footprint means it can run on 64GB and 96GB machines, not just the 128GB configuration mentioned in the README. - Antirez himself confirmed the implementation is much faster and noted 64GB support is achievable with minor changes. **The pushback** - Published benchmarks lack context — total render time is highly dependent on mode, resolution, and duration, making the headline numbers hard to evaluate. - Current speed is still slow on older Apple Silicon (e.g., M1 Max), and memory bandwidth differences between Pro and Max chips are significant. - No quality comparison against commercial alternatives like Veo is provided. **By community** - hackernews (positive): Enthusiastic reception focused on antirez's prolific output and the real-world speed gains over ComfyUI, with practical discussion of memory requirements and benchmark clarity. **Hottest debate:** Whether the published benchmark numbers are meaningful without specifying mode, resolution, and duration. **Open questions** - How does output quality compare to commercial video generation services like Veo? - Will indexed attention support (mentioned by MiniMax in an AMA) be implemented, and how much additional speedup would it provide? - How much slower will performance be on older Apple Silicon like M1 Max compared to M5 Max? **Highlights** > 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. [0] https://huggingface.co/Abiray/MiniMax-H3-GGUF/tree/main/unet > — [Meleagris on hackernews · 3 comments](https://news.ycombinator.com/item?id=49252931) > 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. > — [antirez on hackernews · 1 comments](https://news.ycombinator.com/item?id=49254780) > This repo looks neat, but I hope they had some more clear benchmarks because that time (74.58) 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) and duration (5-15 seconds). > — [vunderba on hackernews](https://news.ycombinator.com/item?id=49253133) > This repo looks neat, but I hope they had some more clear benchmarks because that time (74.58) 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) and duration (5-15 seconds). > — [vunderba on hackernews](https://news.ycombinator.com/item?id=49253133) > In the AMA Minimax said that H3 could support indexed attention, that would be a huge speedup! I wonder if there are any news on that. H3 is very cool. > — [antirez on hackernews](https://news.ycombinator.com/item?id=49253985) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=49252179) · 214 points · 34 comments --- Tags: [#c](https://daily.dev/tags/c), [#video-generation](https://daily.dev/tags/video-generation) [View this post on daily.dev](https://daily.dev/posts/github---antirez-h3-c-minimax-h3-inference-engine-for-mac-computers-cpfhxfvjz) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"GitHub - antirez/h3.c: MiniMax H3 inference engine for Mac computers","url":"https://daily.dev/posts/github---antirez-h3-c-minimax-h3-inference-engine-for-mac-computers-cpfhxfvjz","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/github---antirez-h3-c-minimax-h3-inference-engine-for-mac-computers-cpfhxfvjz"},"datePublished":"2026-08-11T04:07:47.532Z","dateModified":"2026-08-14T17:59:19.385Z","description":"h3-metal is a native inference engine for the MiniMax H3 video/audio generation model, optimized for Apple Silicon using Metal. 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