Ross ROSS = Recommend OSS · open-source software intelligence for agents

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

github.com/JustVugg/colibri · homepage · C · Apache-2.0 (permissive) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
JustVugg/colibrimain80

For agents

markdown · JSON · MCP: product_card(name="JustVugg/colibri")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem