[GitHub Trending] JustVugg/colibri

7.3 relevance

Running MoE models on consumer hardware with zero deps is technically deep and novel for AI infrastructure.

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Why It Matters

For a platform engineer focused on AI infrastructure and cost optimization, Colibrì demonstrates that running 744B+ parameter models on commodity hardware is feasible today, reducing dependency on hyperscaler GPUs and enabling private, auditable inference without API gatekeeping.

Key Takeaways

Evaluate Colibrì for running large MoE models on existing heterogeneous hardware by leveraging its multitier memory hierarchy and JIT weight loading to reduce inference costs and hardware lock-in.

Run frontier MoE models on hardware you already own — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦 - JustVugg/colibri

Summary

Colibrì is a pure C inference engine that runs frontier MoE models (744B to 2.8T parameters) on consumer hardware by treating VRAM, RAM, and NVMe as a single multitier memory hierarchy with zero engine dependencies. It supports eight model families including GLM-5.2, Kimi K3, and DeepSeek V4 Flash, each in a single C file, and provides a web dashboard with live expert routing visualization and a measured expert atlas. The engine prioritizes end-to-end correctness over speed, never silently changing precision or router semantics, and uses JIT weight loading with LRU caching and prefetching based on measured routing heat.

Author

JustVugg