colibri vs Ollama
Pure-C, zero-dep MoE runtime that runs GLM-5.2 (744B) on a 25GB-RAM consumer box by streaming experts from disk — VRAM/RAM/NVMe as one tiered hierarchy, never touching precision. — versus — Run Llama, Mistral and other open models locally with a single command and a clean API.
Both run open models locally. Ollama is the multi-model daily driver for models that fit; colibrì is a single-model specialist that makes a 744B MoE fit where nothing else will.
| colibri | Ollama | |
|---|---|---|
| Stars | 17k | 176k |
| Forks | 1.5k | 17k |
| Language | C | Go |
| License | Apache-2.0 | MIT |
| Last activity | today | 2 days ago |
| Topics | local | local |
| Curated connections | 3 | 20 |
colibri — the curator's take
Use it when you want a frontier-scale open MoE (GLM-5.2, 744B) on hardware that can't hold it, and you care about fidelity: forward pass is token-exact against the transformers oracle, and placement only ever changes speed, never semantics. The learning cache pins the experts YOUR workload actually routes, so it genuinely gets faster with use. NOT a general runner — it's a one-model engine; for everyday multi-model local use pick ollama. And expect 0.05–6.8 tok/s depending on tier residency: this is patience-ware for correctness fanatics, not latency-sensitive serving.
Ollama — the curator's take
Run Llama, Mistral and other open models locally with a single command and a clean API.