I built a Rust inference engine that streams MoE expert weights from NVMe SSDs, no GPU required
A developer has created a Rust inference engine that streams Mixture-of-Experts (MoE) expert weights from NVMe SSDs, eliminating the need for a GPU. This approach leverages the speed of PCIe Gen5 arrays to treat SSDs as a primary memory tier for large language model inference. The project, called Micro-Expert-Router, aims to make advanced AI models more accessible by reducing hardware requirements.
- ▪The Rust inference engine allows for streaming of MoE expert weights directly from NVMe SSDs.
- ▪This method significantly reduces the active memory footprint required for running large models.
- ▪The project is based on Apple's research and aims to provide an open-source implementation of efficient LLM inference.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3953463) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Randy AP Posted on May 27 I built a Rust inference engine that streams MoE expert weights from NVMe SSDs, no GPU required #ai #rust #moe Most people trying to run Mixtral or DeepSeek-V3 locally hit the same wall: they don't have 80GB of VRAM. The common answer is "get better hardware." I wanted to see if there was another way. The idea is straightforward.
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