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Automated AI research is now producing models people can download and run runs entirely on a local machine under an Apache 2.0 license.
Recursive Flint, a 40B mixture-of-experts coding model, was built with the Eureka Machine, a loop that proposes ideas, implements them, tests them and uses the results to choose what to try next.
Flint writes code at 125+ tokens per second on a MacBook Pro, which works out to about 90 words a second, roughly 20x faster than you can read it.
And it has pushed the Pareto frontier on local coding.
On a 48GB MacBook, local models used to make you pick between smart but slow (Qwen3.8 27B scores highest but runs at about 10 tokens per second) and fast but weak (tiny Qwen3.5 models).
Recursive Flint sits almost as high on quality (about 81% average against about 85%) while running roughly 7x faster across a long 128K-token session, and the shaded area marks the speed and quality combination no model offered before.
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
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