Whether Flint can run on a laptop starts with whether memory reaches 48 GB. Flint is the open-source coding model released by Recursive on 21 October 2026, with about 39.8B total parameters and about 2.5B activated per token, a context of 262,144 tokens according to the model card, and an Apache 2.0 licence. The official local route is clear: 4-bit quantised weights of about 22.6 GB to 22.9 GB, at least 48 GB of unified memory or GPU memory recommended, and on Apple silicon an M4 Pro, M5 Pro, or M5 Max is needed to run the official inference engine. If memory does not reach that level, do not download yet; once downloaded, it will not fit and will not run stably.
The reason Flint runs fast lies in the activated count. All 40B parameters must fit in memory, but each token only computes about 2.5B, so the per-step compute is small, and the official engine on an M4 Pro can reach decode speeds above 125 tokens per second on short contexts. The speed figures come with conditions: short context, single-stream decoding, and the official engine; as context lengthens, the memory taken by the key-value cache grows and speed falls, so long-repository Q&A needs a small-scale test first before estimating time. Weight formats must also be chosen correctly: BF16 full precision is about 80 GB and is aimed at data-centre graphics cards; laptops should use the official INT4 or NVFP4 quantised builds, and community GGUF builds require confirming that the toolchain already supports the Flint architecture, since older llama.cpp builds may not recognise the new architecture. Flint is a coding-specialist model: writing code, exploring repositories, fixing bugs, and writing tests are its main job; everyday chat and general Q&A are not its strengths, so do not compare it with a general-purpose chat model.
Who should try it, and who should not yet
People suited to trying it are those with 48 GB or more of memory on a Mac or a 64 GB graphics card, who genuinely need to run a coding agent locally; start with the official quantised weights and the official engine, get short contexts working, then lengthen them. People who should not try yet are users of ordinary laptops with 16 GB or 24 GB of memory: the weights will not fit, and forcing it will only cause repeated paging and unusably slow running; and people who only occasionally ask a couple of coding questions, who will be faster using a hosted API or a free online entry point. Before deciding, measure three things: memory capacity, remaining disk space, and the context length of the repository to be handled; only download when all three pass, and if any one fails, use the hosted service first. Running a model locally saves per-use billing and costs hardware and time; the bill must be counted together.
