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Colibri (GitHub Repo)
Colibrì is a zero-dependency C inference engine that treats disk, RAM, and VRAM as a single hierarchy, allowing massive 2.8T parameter models to run on consumer hardware.
What: The engine uses 'AI memory multitiering' to stream experts in a Mixture-of-Experts (MoE) architecture from NVMe storage on-demand, reducing the required VRAM to a fraction of the total model size.
Why it matters: This proves that frontier-scale intelligence can be accessed on commodity laptops if inference engines are engineered to prioritize smart memory tiering over naive, full-resident model loading.
Takeaway: If you have a large MoE model checkpoint, try the Colibrì `coli chat` command to see if your local storage bandwidth can support inference without a high-end GPU.
Deep dive
- Colibrì supports 744B to 2.8T parameter MoE models via pure C code with no Python or BLAS dependencies.
- It treats VRAM, RAM, and storage as a single memory hierarchy for weight placement.
- Employs routing-aware prefetching to hide the latency of streaming experts from disk.
- Includes a 'learning cache' that pins frequently accessed experts to faster memory tiers over time.
- Supports optional CUDA, Metal, and Vulkan backends to further accelerate expert processing.
- Enables conversational state persistence through compressed Key-Value (KV) caching.
Decoder
- Mixture-of-Experts (MoE): A model architecture where only a subset of the model's total parameters ('experts') is activated for any given input, allowing large models to be computationally efficient.
- Inference engine: The software responsible for running a trained neural network model to generate predictions or content.
- VRAM: Video Random Access Memory; dedicated memory used by the GPU to store model weights during high-speed inference.
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