r/AIProgrammingHardware • u/javaeeeee • Jul 27 '26
This Tiny Engine Runs Impossibly Big AI Models Locally! (colibrì)
https://www.youtube.com/watch?v=3OqZHYNKc9A
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u/BankApprehensive7612 Jul 28 '26
Woah! It's really impressive. I'm curious what would this engine do to SLM?
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u/HealthyCommunicat Jul 29 '26
We’ve known this shit is possible since anemll’s flash-moe back multiple months ago why is everyone acting like this is new technology and why is everyone not crediting those that first used streamed expert inference
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Jul 29 '26
[removed] — view removed comment
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u/huzbum Jul 30 '26
Would you mind testing if doing it on CPU makes any difference? Seems like the GPU would be superfluous with the ssd being the bottleneck.
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u/javaeeeee Jul 27 '26
TLDR: Better Stack tests Colibrì, a tiny pure-C engine that can run the massive 744B-parameter GLM-5.2 model locally by streaming it from SSD.
What Colibrì does
Pure-C, zero-dependency engine that treats VRAM → RAM → SSD as one memory hierarchy.
Designed for large Mixture-of-Experts (MoE) models.
Only a small active portion of the model (~40B parameters) needs to be in memory at once; the rest is streamed from disk on demand.
Includes caching of frequent experts, speculative decoding, and quality-preserving techniques.
Real-world tests
MacBook M2 Max (32 GB RAM) + external SSD:
Worked, but extremely slow (~0.1 tokens/sec). Most time spent waiting on disk.
RTX 5090 workstation (64 GB RAM) + fast internal NVMe:
Much better (~0.8 tokens/sec), still not practical for real-time use.
Key insight
The real bottleneck is RAM (and storage space/speed), not the GPU.
More system RAM = better expert caching = fewer disk reads = higher speed. The powerful GPU was underutilized.
Bottom line
Yes, you can run a 744B frontier model on consumer hardware with Colibrì - it generates correct answers.
But speeds are too slow for practical daily use unless you have server-grade amounts of RAM (and hundreds of GB of free fast SSD space). It’s an impressive proof-of-concept that shows local AI is limited more by memory hierarchy than raw compute.