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og:description: Recursive released Flint, a mixture-of-experts model built for fast agentic coding on local hardware. It has 40 billion total parameters but activates only 2.5...
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# Recursive Flint is a mixture-of-experts model built for fast agentic coding on local hardware. It has 40 billion total parameters but activates just 2.5 billion per token. It was built from scratch by a small team of people and agents in under four m

**[ml-research](https://daily.dev/sources/mlresearch)** · [@andrewma](https://daily.dev/andrewma) · 3 upvotes · 0 comments

## Summary

Recursive released Flint, a mixture-of-experts model built for fast agentic coding on local hardware. It has 40 billion total parameters but activates only 2.5 billion per token, and the team built it from scratch in under four months. Flint leads MoE models under 50 billion total parameters on SWE-Bench Pro v2, Terminal-Bench 2.1, and SWE-Bench Verified, and generates more than 125 tokens per second on an M4 Pro MacBook Pro at short contexts. It supports context windows of up to 512k tokens, works with llama.cpp, Ollama, vLLM, and SGLang, and is available on Hugging Face under Apache 2.0.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://recursive.com/updates/introducing-recursive-flint>

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---

Tags: [#data-science](https://daily.dev/tags/data-science), [#ai-coding](https://daily.dev/tags/ai-coding), [#local-ai](https://daily.dev/tags/local-ai), [#mixture-of-experts](https://daily.dev/tags/mixture-of-experts), [#huggingface](https://daily.dev/tags/huggingface)

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