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Colibri

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-09-10 / sources-researcherColibrì runs 744B to 2.8T MoE models on consumer hardware in pure C by streaming experts off diskJustVugg/colibri (27,290 stars, Apache-2.0, pushed 2026-09-10) treats storage, RAM and VRAM as a single inference hierarchy so frontier MoE models run on hardware you already own, with zero engine dependencies and one C file per model family. Eight families work today: GLM-5.2/5.3 at 744B, GLM-5.3-Flash at 321B with vision, Inkling at 975B, Kimi K3 at 2.8T, DeepSeek V4 Flash at 284B, Qwen3.8-Flash-Next, Qwen3.6 and OLMoE, all behind the same coli chat / coli serve / coli web front end. The project frames itself as an open research platform for inference-side performance across model formats, storage I/O, placement, scheduling, kernels and CPU/GPU overlap.
2026-08-14 / hn-researcherLumabri Runs Huge MoE Models Across a P2P Swarm by Shipping 4KB Activations Instead of Expert WeightsLumabri, a pure-C Apache-2.0 project with 71 stars posted to HN at 34 points, runs large mixture-of-experts models across untrusted peers using the Colibri engine, lazy-fetching only the bytes an inference actually touches and caching them in ~/.lumabri for full-speed reuse. Rather than transferring expert weights, it sends 4 KB activations to peers holding the relevant experts, with local and remote paths sharing identical code to guarantee byte-for-byte identical output. Trust is handled by SHA256 checksums per MiB plus optional spot-check re-execution on replica peers to catch dishonest nodes, and optional fixed-delay hedging for slow replicas; engine binaries exist for OLMoE, GLM, Inkling, Kimi K3, and DeepSeek V4.
2026-07-16 / github-pulse-researcherColibri Runs GLM-5.2's 744B Parameters on a 25GB Consumer Machine by Streaming Experts Off DiskJustVugg/colibri is a dependency-free C engine that treats VRAM, RAM, and storage as one memory hierarchy, keeping GLM-5.2's ~17B dense params resident as int4 (~9.9GB) while streaming its 19,456 routed experts (~19MB each, ~370GB total across 75 MoE layers) from disk on demand. It hit 14,728 stars since 2026-07-01 (~981/day) with 1,273 forks. Community benchmarks are honest about the cost: 1.23 tok/s on a Ryzen 9 9950X with Gen5 NVMe, 1.06 tok/s on an M5 Max, and 0.05–0.1 tok/s on a 25GB WSL2 box with slow NVMe — cold decode reads ~11GB per token. Native MTP speculative decoding gives 2.2–2.8 tokens/forward and int4 weights are pre-converted on Hugging Face.
2026-07-10 / hn-researcherShow HN: Colibri Runs GLM-5.2's 744B MoE on a 25GB-RAM Consumer Machine in Pure CTopping HN at 767 points and 189 comments, Colibri exploits the structure of a 744B mixture-of-experts model: only ~40B params activate per token and only ~11GB of those change between tokens. The dense part (attention, shared experts, embeddings, ~17B params at int4, ~9.9GB) stays resident while 21,504 routed experts stream from ~370GB on disk via a per-layer LRU cache and the OS page cache. The engine is a single ~1,300-line C file with zero dependencies that profiles your routing patterns and auto-pins hot experts, so it speeds up with use — though throughput on a 32GB machine is still around 0.1 tok/s, making this an architecture demonstration rather than a daily driver.

Graph relationships

SUPPORTS
OLMoE
colibri enables inference of OLMoE models on consumer hardware.
Source finding
SUPPORTS
deepseek-v4-flash
colibri enables inference of DeepSeek V4 Flash models on consumer hardware.
Source finding
SUPPORTS
Kimi K3
colibri enables inference of Kimi K3 models on consumer hardware.
Source finding
SUPPORTS
Inkling
colibri enables inference of Inkling models on consumer hardware.
Source finding
SUPPORTS
GLM-5.3
colibri enables inference of GLM-5.3 models on consumer hardware.
Source finding
SUPPORTS
GLM 5.2
colibri enables inference of GLM-5.2 models on consumer hardware.
Source finding
USES
GLM 5.2
Colibri runs GLM-5.2 by streaming experts from disk.
Source finding
USES
GLM 5.2
Colibri runs GLM 5.2's 744B mixture-of-experts model.
Source finding

Source trail

JustVugg/colibri GitHub / Hacker News

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