Mesh connects idle Nvidia GPUs into open AI compute network

Running a 235 billion-parameter AI model used to require renting a small fortune’s worth of cloud compute. Mesh LLM wants you to do it with the GPUs already sitting in your house, your neighbor’s house, and maybe a few strangers on the internet. The open-source project, led by developer Michael Neale and endorsed by Jack Dorsey, pools idle Nvidia GPUs across multiple machines into a peer-to-peer network capable of distributed inference on large language models. How it actually works Mesh LLM creates a decentralized mesh of GPUs spanning macOS, Linux, and Windows machines, with a heavy emphasis on Nvidia’s CUDA architecture. Instead of routing your AI queries to OpenAI’s or Google’s data centers, the system keeps everything local or distributed across connected nodes with zero central server involvement. The project exposes an OpenAI-compatible API, which means developers can swap in Mesh as a drop-in replacement for cloud-hosted models without rewriting their applications. Under the hood, a novel pipeline approach called “Skippy” partitions massive models across multiple nodes. A 235 billion-parameter Mixture of Experts model, for instance, gets sliced up and spread across whatever...