AI Briefingcolibri: Running a 744B MoE on a Consumer PC
Key point
An inference engine written in C has been released that keeps a large MoE model's weights on disk and streams only the parts needed, allowing it to run even on low-spec PCs.
Details
colibri is a pure C-based inference engine designed to run a 744B-parameter MoE (Mixture-of-Experts) model on an ordinary consumer PC with roughly 25GB RAM.
The core mechanism, instead of loading the entire model into memory, keeps only the always-used Dense portion (about 17B parameters) resident in RAM, while the remaining Expert weights are stored on disk and streamed in only as needed during token generation. This allows huge models to run without data-center-grade GPUs.
Key technical features include:
- Intelligent caching: Provides LRU cache and predictive caching that learns usage patterns to pre-pin frequently called experts in RAM.
- MLA and compressed KV cache: Implements GLM-5.2's MLA attention, compressing the KV cache size by about 57x compared to before.
- MTP (Multi-Token Prediction) support: Improves inference speed through native MTP speculative decoding, and increases output accuracy for specific formats like JSON through Grammar Constrained functionality.
- Lightweight design: A single C file-based engine that works without Python or a GPU, and includes integer matrix multiplication kernels using AVX2 instructions.
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