--- title: antirez built a native Metal inference engine for... description: antirez (Salvatore Sanfilippo, Redis creator) built h3.c, a from-scratch C inference engine for MiniMax H3 targeting Apple Silicon via Metal. MiniMax H3 is an... canonical: https://daily.dev/posts/antirez-built-a-native-metal-inference-engine-for-minimax-h3-and-it-s-fast-lj1jyst7b twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: antirez built a native Metal inference engine for MiniMax H3, and it's fast | daily.dev og:description: antirez (Salvatore Sanfilippo, Redis creator) built h3.c, a from-scratch C inference engine for MiniMax H3 targeting Apple Silicon via Metal. MiniMax H3 is an... og:url: https://daily.dev/posts/antirez-built-a-native-metal-inference-engine-for-minimax-h3-and-it-s-fast-lj1jyst7b og:image: https://api.daily.dev/og/posts/LJ1jYsT7B.png og:image:alt: antirez built a native Metal inference engine for MiniMax H3, and it's fast 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. # antirez built a native Metal inference engine for MiniMax H3, and it's fast **[Trends](https://daily.dev/sources/trends)** · 2 min read · 1 upvotes · 0 comments ## Summary antirez (Salvatore Sanfilippo, Redis creator) built h3.c, a from-scratch C inference engine for MiniMax H3 targeting Apple Silicon via Metal. MiniMax H3 is an open-weight video generation model that topped Design Arena's leaderboard, beating Seedance 2.0, Grok Imagine Video 1.5, and Gemini Omni Flash. The engine achieves a 4-step denoise in ~3.5 seconds on M5 Max versus 26.4 seconds for the reference PyTorch path. Key optimizations include fused DiT kernels, BF16 and int8 paths via Metal 4/TensorOps, activation memory aliasing, and streamed prompt encoding. A full 50-layer 512x512 render with int8 QKV takes ~19 seconds. The full model is 66 GB, with pruned versions at 21 GB requiring 12 GB VRAM minimum. H3 supports text-to-video, image-to-video, and reference-to-video with built-in audio generation. Licensing is permissive for commercial use under $20M revenue, with separate license requirements for EU, UK, Korean, and US users. ## Content MiniMax dropped H3, an open-weight video generation model, and the ecosystem moved on it fast enough to make you wonder if anyone slept. The headline number: H3 just took #1 across three video categories on Design Arena, beating Seedance 2.0, Grok Imagine Video 1.5, and Gemini Omni Flash. As trikcode put it, "another category dominated by open-weight models." That's the real story here, not the model itself but what it says about where the ceiling is right now: closed frontier labs aren't automatically winning video gen anymore. Then antirez showed up. Within days of the open-source release, he'd written h3.c, a native inference engine for the model built specifically for Apple Silicon. He posted the Metal implementation with a shrug: "Enjoy, modify, and so forth." He also flagged that it contains code from liuliu and explicitly invited him to take back whatever parts are useful for drawthings, in case there are H3 plans there. That's the open-source loop working exactly as intended, one person's optimization becoming another project's foundation within the same week. The performance numbers in that Metal port are the part worth sitting with. On an M5 Max, a 4-step denoise runs in about 3.5 seconds, versus 26.4 seconds for a full 29-pass reference render. A 50-layer, 19-transition 512x512 render finishes in roughly 19 seconds using int8 QKV. This is a full-size video/audio generation model with first/last-frame conditioning and image/video/audio referencing, running fast enough on consumer Apple hardware that ## Questions this post answers ### How fast is the h3.c Metal inference engine for MiniMax H3 compared to PyTorch on Apple Silicon? On an M5 Max, h3.c completes a 4-step denoise in ~3.5 seconds, versus 26.4 seconds for the reference PyTorch path running 29 passes. A full 50-layer, 512x512 render with int8 QKV takes around 19 seconds. The speedup comes from fused DiT kernels, BF16 and int8 paths via Metal 4/TensorOps, activation memory aliasing, and streamed prompt encoding. _Engineers optimizing ML inference on Apple Silicon track projects like h3.c on daily.dev._ ### What are the VRAM requirements to run MiniMax H3 locally? The full MiniMax H3 model is 66 GB, but pruned versions run at 21 GB and require a minimum of 12 GB VRAM. Community members have pushed it further down to 5 GB VRAM at 480p resolution. The model supports text-to-video, image-to-video, and reference-to-video with built-in audio generation. _Those running large video models locally find hardware and quantization tradeoffs like these covered on daily.dev._ ### What are the commercial licensing restrictions for MiniMax H3? MiniMax H3 is permissively licensed for commercial use for organizations under $20M in revenue. Users in the EU, UK, South Korea, and the United States must submit a separate license application, which is a common friction point for open-weight models targeting international deployment. _Developers evaluating open-weight models for commercial products track licensing details like these on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 2 discussions and 37 comments across hackernews, x (as of 2026-08-14). **TL;DR:** Replies focus on the broader open video model ecosystem rather than the Metal port itself, noting how quickly derivative tools and prompt libraries emerge around open releases like H3. GitHub - antirez/h3.c: MiniMax H3 inference engine for Mac computers: 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:** 69% positive · 26% mixed · 5% skeptical **The case for** - The fast emergence of quantized builds, local runners, and prompt libraries around an open model is seen as a healthy ecosystem signal. - The engine delivers dramatic speed improvements over existing ComfyUI workflows — what took over an hour now takes a few minutes on comparable hardware. - Prompts are viewed as portable, reusable assets across tools. - 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. - x (positive): Short replies express enthusiasm about the open model ecosystem moving quickly, with no substantive criticism raised. **Hottest debate:** GitHub - antirez/h3.c: MiniMax H3 inference engine for Mac computers: 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** > @atlas_cloud_ai The useful signal is the derivative-model count. Once an open video model has quantized builds, local runners, and prompt libraries within a week, the ecosystem starts moving faster than the official release notes. > — [AlexshevPm on x](https://x.com/AlexshevPm/status/2088272982468612285) > 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) > 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) > 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) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=49252179) · 214 points · 34 comments - [x](https://x.com/atlas_cloud_ai/status/2088270530331959583) · 4 points · 3 comments --- Tags: [#video-generation](https://daily.dev/tags/video-generation) [View this post on daily.dev](https://daily.dev/posts/antirez-built-a-native-metal-inference-engine-for-minimax-h3-and-it-s-fast-lj1jyst7b) ```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":"antirez built a native Metal inference engine for MiniMax H3, and it's fast","url":"https://daily.dev/posts/antirez-built-a-native-metal-inference-engine-for-minimax-h3-and-it-s-fast-lj1jyst7b","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/antirez-built-a-native-metal-inference-engine-for-minimax-h3-and-it-s-fast-lj1jyst7b"},"datePublished":"2026-08-11T04:08:39.124Z","dateModified":"2026-08-14T17:59:24.964Z","description":"antirez (Salvatore Sanfilippo, Redis creator) built h3.c, a from-scratch C inference engine for MiniMax H3 targeting Apple Silicon via Metal. 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