JustVugg/colibri

Tiny engine, immense model. Run frontier MoE models — 744B to 2.8T parameters — on consumer and heterogeneous hardware, in pure C with zero engine dependencies, by treating storage, RAM, and VRAM as a single inference hierarchy (AI memory multitiering).

Eight families run today: GLM-5.2/5.3 (744B), GLM-5.3-Flash (321B, with vision), Inkling (975B), Kimi K3 (2.8T), DeepSeek V4 Flash (284B), Qwen3.8-Flash-Next (125B + 51B n-gram), Qwen3.6 (35B-A3B) and OLMoE (7B) — one C file each, the same coli chat / coli serve / coli web front end. Full roster ↓

Colibrì is an inference engine you can run today, and an open research platform. Its primary goal is to pursue inference-side performance across the entire software/hardware boundary — model formats, memory hierarchy, storage I/O, placement, scheduling, kernels, speculation, and CPU/GPU overlap — so large models depend less on scarce hardware and cost less to run.

Colibrì treats VRAM, RAM, and storage as a single multitier hierarchy, and it is deliberately a place to test aggressive systems ideas — so there is no SLA on speed, and a hard guarantee on semantics: experiments must earn their place through reproducible end-to-end measurements, and the default policy never silently changes model precision or router semantics. Insufficient fast memory may reduce speed; it must not quietly redefine the model.