Recursive Flint 40B 开源模型本地跑出 125 token/秒编码速度
一个 40B 的开源编码模型，MacBook 本地就能跑到 125 token/秒，速度比 Qwen3.8 27B 快 7 倍，质量只差 4 个点。
Recursive Flint 是一个 40B 参数的混合专家编码模型，采用 Apache 2.0 许可证，可完全在本地机器上运行。它由 Eureka Machine 自动化研究循环生成，该循环自动提出想法、实现、测试并用结果决定下一步方向。在 MacBook Pro 上它达到 125+ token/秒的编码速度，约合每秒 90 个词。在 48GB MacBook 的 128K-token 长会话中，其质量约 81%，略低于 Qwen3.8 27B 的约 85%，但速度快约 7 倍。
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.
