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

TurboQuant vector quantization targets KV cache bloat, aiming to cut LLM memory use by 6x while preserving benchmark accuracy ...

On March 24, 2026, Google Research announced a new suite of compression techniques for large-scale language models and vector search engines: TurboQuant, PolarQuant, and Quantized ...

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

Google's TurboQuant reduces the KV cache of large language models to 3 bits. Accuracy is said to remain, speed to multiply. Google Research has published new technical details about its compression ...

Google has unveiled a new AI memory compression technology called TurboQuant, and the announcement has already had a measurable impact on the semiconductor market. The technology is designed to reduce ...

A new quantum sensing approach could dramatically improve how scientists measure low-frequency electric fields, a task that ...

Google Research published TurboQuant on Tuesday, a training-free compression algorithm that quantizes LLM KV caches down to 3 bits without any loss in model accuracy. In benchmarks on Nvidia H100 GPUs ...

Investors in fast-rising memory storage stocks may be seeking to lock in profits after news from Google’s parent company appeared to have rattled the industry. Thanks for the memory?

TL;DR: Google developed three AI compression algorithms-TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss-that reduce large language models' KV cache memory by at least six times without ...