mu-hashmi/mlx-moe

mlx-moe addresses a specific pain point for the Mac enthusiast community: running massive models like Mixtral 8x22B or DeepSeek-V2 on hardware with 16GB-32GB of RAM. By swapping experts from the SSD on-demand, it bypasses hard RAM limits. However, the project has negligible traction (2 stars) and faces a massive technical hurdle: SSD I/O latency. Even on fast Mac NVMe drives, loading multiple gigabytes of expert weights per token is orders of magnitude slower than unified memory access, likely resulting in sub-1 token/sec performance. From a competitive standpoint, this is a classic 'feature-not-product' scenario. The Apple MLX team or the llama.cpp maintainers could implement more efficient mmap-based demand paging or MoE-specific caching that would render this standalone script obsolete. Its low velocity and minimal community engagement suggest it is a personal experiment rather than a foundational tool. Projects like PowerInfer are exploring more sophisticated 'predictive' offloading which poses a significant architectural threat to simple SSD-swapping approaches.