Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

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

Intelligent Cache Replacement Algorithm for Web Proxy Caching based on Multi-level K-means Clustering

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 ...
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.
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 ...
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 ...