JustVugg/colibri
Run frontier MoE models on hardware you already own — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦 observed · 2026-08-28
Health v2 · maintenance only
80/100
- Activity 99
- Release rhythm 99
- Longevity 4
Flags: young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 2
- age_days: 63
- days_rel: 9
- days_push: 7
- n_releases_24m: 12
Adoption not part of the score
26251 stars · 2872 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Colibrì is a pure-C, zero-dependency inference engine that runs frontier Mixture-of-Experts models (744B to 2.8T parameters) on consumer hardware by streaming expert weights from disk and treating VRAM, RAM, and storage as a single memory hierarchy. It ships per-model engines with a unified front end (chat, serve, web dashboard) and validates outputs token-exact against reference implementations.
Use cases
- run trillion-parameter MoE models on a consumer PC
- stream LLM expert weights from NVMe instead of loading into RAM
- self-host large language models without a datacenter
- serve a 744B model from a C engine with no dependencies
- run frontier models on Apple Silicon via Metal
- inspect which MoE experts fire with a live dashboard
- run LLM inference on CPU with limited VRAM
When to choose
- you want to run very large MoE models on hardware with limited VRAM/RAM
- you need a dependency-free, pure-C inference engine you can audit and hack on
- you want token-exact outputs matching reference implementations
- you're researching inference-side systems: memory tiering, prefetching, CPU/GPU overlap
When to avoid
- you need guaranteed latency or an SLA for production serving
- you need broad model support beyond the six supported families
- you want a mature ecosystem with extensive tooling and integrations
- you need fast throughput on a single consumer machine — streaming from disk trades speed for capacity
Facets
library · maturity active
llm-inference llm-training gpu-computing cli http-server chat-interface large-language-models machine-learning artificial-intelligence developer-tools self-hosted windows cpp cli self-hosted cross-platform mixture-of-experts inference-engine expert-streaming memory-tiering pure-c zero-dependencies quantization int4 nvme-streaming cpu-inference apple-silicon metal linux macos gpu
2 sources
- readme: https://github.com/JustVugg/colibri · fetched 2026-08-28 · 28cfb0c42536
- homepage: https://justvugg.github.io/colibri · fetched 2026-08-29 · 1305a77ec2f3
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| JustVugg/colibri | main | 80 |
For agents
markdown · JSON · MCP: product_card(name="JustVugg/colibri")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem