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Kapitsa engine enables Flint 40B model to run at 128 tok/s on MacBook Pro M4 Pro

By PulseAugur Editorial · [1 sources] ·

An open-source inference engine called Kapitsa has been developed to optimize large language models for Apple Silicon chips, including the new M4 Pro. This engine allows models like Recursive's Flint 40B to run efficiently on MacBooks with 48GB of RAM, achieving speeds of 128 tokens per second. Kapitsa utilizes custom Metal kernels for performance and is compatible with various API formats, aiming to bring powerful AI capabilities to consumer hardware. AI

IMPACT Optimizes LLM performance on consumer Apple hardware, potentially increasing accessibility for local AI model deployment.

RANK_REASON The item describes a new open-source engine and a model's performance benchmark on consumer hardware, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Kapitsa engine enables Flint 40B model to run at 128 tok/s on MacBook Pro M4 Pro

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The item describes a new open-source engine and a model's performance benchmark on consumer hardware, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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model release, infra
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High
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1 days old
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/DotaMate ·

    Benchmark: Recursive's Flint 40B A2.5B Q4 achieves 128 tok/s on MacBook Pro M4 Pro 48GB using Kapitsa, an open-source Apple Silicon optimized engine, Claude/Codex/Pi/OpenCode compatible.

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xcgzp4/benchmark_recursives_flint_40b_a25b_q4_achieves/"> <img alt="Benchmark: Recursive's Flint 40B A2.5B Q4 achieves 128 tok/s on MacBook Pro M4 Pro 48GB using Kapitsa, an open-source Apple Silicon optimize…

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