A research article by Horace He and the Thinking Machines Lab (X-OpenAI CTO Mira Murati founded) addresses a long-standing issue in large language models (LLMs). Even with greedy decoding bu setting ...

“Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the ...

“Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI ...

Very few organizations have enough iron to train a large language model in a reasonably short amount of time, and that is why most will be grabbing pre-trained models and then retraining the ...

Google says its new TurboQuant method could improve how efficiently AI models run by compressing the key-value cache used in LLM inference and supporting more efficient vector search. In tests on ...