LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Rearranging the computations and hardware used to serve large language ...
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 ...
A technical paper titled ā€œLLM in a flash: Efficient Large Language Model Inference with Limited Memoryā€ was published by researchers at Apple. ā€œLarge language models (LLMs) are central to modern ...
ā€œLarge 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 ...
Serving Large Language Models (LLMs) at scale is complex. Modern LLMs now exceed the memory and compute capacity of a single GPU or even a single multi-GPU node. As a result, inference workloads for ...
Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...
The latest trends and issues around the use of open source software in the enterprise. Snowflake says it will now host the Llama 3.1 collection of multilingual open source large language models (LLMs) ...
OpenAI and Broadcom are debuting 'Jalapeño,' OpenAI's first Intelligence Processor: an accelerator architected around OpenAI's vision for the future of LLM inference. According to the OpenAI and ...