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

As organizations increasingly rely on algorithms to rank candidates for jobs, university spots, and financial services, a new ...

Abstract: Distributed cache is capable of accelerating the process of retrieving an enormous amount of data. In order to optimize the cache performance in distributed environment, we present an ...

TurboQuant is a compression algorithm introduced by Google Research (Zandieh et al.) at ICLR 2026 that solves the primary memory bottleneck in large language model inference: the key-value (KV) cache.

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI chatbots. The cache grows as conversations lengthen, ...

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.

If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. Specifically, the Key-Value (KV) cache size ...

Abstract: With the popularity of cloud services, Cloud Block Storage (CBS) systems have been widely deployed by cloud providers. Cloud cache plays a vital role in maintaining high and stable ...