Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in ...

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...

The dynamic interplay between processor speed and memory access times has rendered cache performance a critical determinant of computing efficiency. As modern systems increasingly rely on hierarchical ...

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...

Google researchers have proposed TurboQuant, a two-stage quantization method that, according to a recent arXiv preprint, can ...

A new technical paper titled “ARCANE: Adaptive RISC-V Cache Architecture for Near-memory Extensions” was published by researchers at Politecnico di Torino and EPFL. Abstract “Modern data-driven ...

So far, so futile. Both these approaches are doomed by their respective medium being orders of magnitude slower to access and ...

The Spectre and Meltdown vulnerabilities in 2018 exposed computer memory as an easy target for hackers to inject malicious code and steal data. The aftermath spurred the adoption of memory-safe chips ...