Flint 40B: Frontier Local LLM (Fast 125 Tps on MacBook)

Recursive SI released Flint, its first model, an open-weights local coding model built with the company's automated research system, the Eureka Machine, CEO Richard Socher announced on X. Recursive SI claims Flint runs at 125+ tokens/sec on an M4 Pro MacBook Pro and 150+ tokens/sec on a 64GB NVIDIA RTX Spark, up to 2x faster than comparable models on short contexts and 5x+ faster than dense models with similar coding scores, and that it is the best-performing MoE model under 50B on SWE-bench Pro v2, Terminal-Bench 2.1 and SWE-bench Verified. The model was built from scratch in under 4 months, with weights available on Hugging Face and free trials on OpenCode and Pidotdev.

Richard Socher on X: "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… / X Richard Socher on X: "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." 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. 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. Flint is the best-performing model in its class MoE models under 50B on agentic coding benchmarks like SWE-bench Pro v2, Terminal-Bench 2.1, and SWE-bench Verified. It also rivals similarly sized dense models that are much slower to run. Flint is also the fastest model in its class: • 125+ tokens/sec on an M4 Pro MacBook Pro • 150+ tokens/sec on a 64GB NVIDIA RTX Spark Up to 2x faster than comparable models on short contexts, and 5x+ faster than dense models with similar coding scores. Your data stays on yourShow more Flint is also a real-world test of the Eureka Machine, our system for automated AI research. It runs the full research loop: ideation, implementation, and validation. Then it uses the results to decide what to try next. We used it in nearly every part of how Flint was built.Show more