I Ran Claude Code on My MacBook With vllm-mlx — It Embarrassed llama.cpp by 87%
A user successfully ran Anthropic's Claude code on their MacBook using the vllm-mlx library. This setup significantly outperformed llama.cpp, achieving an 87% improvement in performance. The author expressed surprise at the ease and efficiency of running the model locally. AI
IMPACT Demonstrates the increasing feasibility of running advanced LLMs on local consumer hardware, potentially reducing reliance on cloud services.