Ars Technica

Google’s TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

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 ...
TechCrunch

Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it ‘Pied Piper’

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 ...
IEEE

An Efficient Hybrid Cache Replacement Policy for Cloud Block Storage

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 ...
IEEE

LiC: Low-Cost Cache Replacement Algorithm for All Cache Levels

Abstract: Modern processors use caches to reduce memory access time. However, their limited size leads to frequent misses, requiring an efficient replacement policy. The Least Recently Used (LRU) ...
marktechpost

Google Introduces TurboQuant: A New Compression Algorithm that Reduces LLM Key-Value Cache Memory by 6x and Delivers Up to 8x Speedup, All with Zero Accuracy Loss

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 ...
GitHub

TurboQuant Skill — LLM KV Cache Compression for AI Coding Agents

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.