Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits. MeMo, a ...

Researchers at the Tokyo-based startup Sakana AI have developed a new technique that enables language models to use memory more efficiently, helping enterprises cut the costs of building applications ...

Generative AI applications don’t need bigger memory, but smarter forgetting. When building LLM apps, start by shaping working memory. You delete a dependency. ChatGPT acknowledges it. Five responses ...

Memory is arguably the most serious constraint on modern AI large language models (LLMs). According to one influential paper, LLM token generation is an inherently memory-bound task, meaning the rate ...

Tensormesh Inc. has hit upon a way to make artificial intelligence inference more efficient by eliminating the need for redundant computations, and its technology is so convincing that several of AI ...

Think of continuous batching as the LLM world’s turbocharger — keeping GPUs busy nonstop and cranking out results up to 20x faster. I discussed how PagedAttention cracked the code on LLM memory chaos ...

The AI was smarter than the person setting it up ...

A new technical paper titled “Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need” was published by NVIDIA. “This paper presents a limit study of ...