Running LLMs locally on personal hardware instead of cloud APIs
by Stackness
LLMsInstead of routing every inference call to a hosted API, developers set up local runtimes such as CoreML on Apple silicon or vLLM style stacks on home GPU servers to run models on their own hardware. This trades some raw capability for privacy, cost control, and offline availability, and increasingly extends to running smaller distilled models directly in the browser via WebGPU. The pattern spans dedicated inference scripts, home-built multi-GPU servers, and browser demos.
Evidence:
- https://gist.github.com/fordnox/e592d0f68b543fd044be8e6d040863a0
- https://www.reddit.com/r/LocalLLaMA/comments/1wfe9zt/3k_128gb_vram_256gb_ram_ddr4_server/
- https://www.reddit.com/r/LocalLLaMA/comments/1wj6c4l/ternary_bonsai_2_27b_just_released_on_hugging/
- https://github.com/mizorewww/laya-coreml#readme