While Large Language Models (LLMs) like GPT-3 and GPT-4 have quickly become synonymous with AI, LLM mass deployments in both training and inference applications have, to date, been predominately cloud ...

The growing imbalance between the amount of data that needs to be processed to train large language models (LLMs) and the inability to move that data back and forth fast enough between memories and ...

Researchers from the University of Edinburgh and NVIDIA have introduced a new method that helps large language models reason more deeply without increasing their size or energy use. The work, ...

Apple researchers have developed a breakthrough framework that dramatically reduces the memory requirements for AI systems engaged in long conversational interactions, a development that could ...

Imagine having a conversation with someone who remembers every detail about your preferences, past discussions, and even the nuances of your personality. It feels natural, seamless, and, most ...

Large language models (LLMs) such as GPT-4o and other modern state-of-the-art generative models like Anthropic’s Claude, Google's PaLM and Meta's Llama have been dominating the AI field recently.

During sleep, the human brain sorts through different memories, consolidating important ones while discarding those that don’t matter. What if AI could do the same? Bilt, a company that offers local ...

A Nature paper describes an innovative analog in-memory computing (IMC) architecture tailored for the attention mechanism in large language models (LLMs). They want to drastically reduce latency and ...