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[ARTICLE · art-133444] src=byteiota.com ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

Colibri: Run a 744B MoE Model Locally on 25GB RAM

Colibri is a new inference architecture that runs GLM-5.2, a 744-billion-parameter Mixture-of-Experts model, locally on 25GB of RAM by streaming expert weights from disk, since the model activates only about 40 billion parameters per token. On a typical CPU-only machine the setup generates roughly 0.05 tokens per second, a speed the architecture's backers frame as acceptable for the right workloads rather than a general-purpose replacement for GPU inference.

read1 min views3 publishedSep 18, 2026

The headline writes itself: run a 744-billion-parameter model on a laptop. The fine print is less spectacular — on a typical CPU-only machine, you’ll clock roughly 0.05 tokens per second. But that framing misses the point. Colibri isn’t a trick. It’s a genuinely novel inference architecture that changes the economics of running frontier-scale models locally, and for the right workloads, it works today. Why Disk Streaming Works for MoE Models Colibri’s core insight is deceptively simple: Mixture-of-Experts models are sparse. GLM-5.2 has 744 billion parameters on paper, but only activates roughly 40 billion per token. For each layer, just 8 […]

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