
flash-moe
Running a big model on a small laptop
The Lens
By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.
Updated Oct 2026
Flash-moe runs Qwen3.5-397B, a 397-billion-parameter model, on a MacBook Pro with 48GB of RAM. It's a Mixture of Experts model, so only a few expert slices fire per token, and flash-moe streams those slices off the SSD instead of holding all 209GB in memory. Pure C, Objective-C and hand-written Metal shaders. Free to read, with a big license caveat.
This is a research demo, not a product. The author measured about 4.4 tokens per second at 4-bit on an M3 Max with tool calling working; the faster 2-bit mode breaks JSON output. Apple Silicon only. You download the weights, repack the experts with the included Python scripts and build with make; one user's issue describes getting it working "with a lot of pain." No paid tier. Solo tinkerers with a big Mac: worth an afternoon and the paper. Teams: no. For real local inference, use llama.cpp, Ollama or MLX.
The catch: there is no LICENSE file, so legally it's all rights reserved and you can't ship anything built on it. A request to add one has sat open since March. The last commit was March 19, 2026, and community ports, including a CUDA version, sit unmerged.
Free vs Self-Hosted vs Paid
fully freeFree: The full source is public on GitHub at no cost: the C/Objective-C inference engine, Metal shaders, chat TUI, weight-packing scripts and the paper. There is no LICENSE file, so no rights to reuse or redistribute are granted.
Self-hosted: Local only, on Apple Silicon. You download the Qwen3.5-397B-A17B weights, repack the experts (about 209GB on disk at 4-bit, 120GB at 2-bit) with the included Python scripts, and build with make. The reference machine is an M3 Max MacBook Pro with 48GB of unified memory and a fast internal SSD.
Paid: No paid tier and no hosted service. The only cost is the hardware and disk space.
No price, but no license either: it's all rights reserved, so treat it as something to study, not something to build on.
What to do by team size
- Solo
- free to study on a 48GB Apple Silicon Mac; not a daily driver
- Small team
- skip it; use llama.cpp, Ollama or MLX
- Medium team
- skip it; no license and no maintenance
- Large team
- skip it; all rights reserved means legal can't approve it
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