Researchers at North Carolina State University have developed a new AI-assisted tool that helps computer architects boost ...
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 ...
As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...
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 ...
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 ...
Abstract: Proxy web caching is usually employed to maximize the efficiency and utilization of the network and the origin servers while reducing the request latency. However, and due to the limited ...
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 ...
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.
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