19.
Liquid AI’s 350M multilingual retrieval models claim 1.5ms latency
Liquid’s LFM2.5 embedding and ColBERT models target ultra-fast multilingual search across 11 languages with end-to-end retrieval latency as low as 1.5ms
@liquidai on Backlist
2 appearances on the backlist front page in the last 30 days.
61.
The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. (x.com)
The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. Our CTO Mathias Lechner, @mlech26l , joins Piotr Mazurek, @tugot17 , from our inference team, to discuss what actually limits token throughput and how