Google Announces TurboQuant — Reduces AI Memory Usage by 6x, Memory Chip Stocks Tumble

Google's research division has announced a new family of compression algorithms called TurboQuant that dramatically reduces memory usage for large language models. In internal tests, TurboQuant demonstrated the ability to reduce key-value (KV) cache memory requirements for LLMs by at least 6x while maintaining model accuracy.

The breakthrough combines two novel methods: PolarQuant, a quantization technique, and Quantized Johnson-Lindenstrauss (QJL), a training and optimization method. The research is scheduled for formal presentation at the ICLR 2026 and AISTATS 2026 conferences.

The announcement of this software-based efficiency gain had an immediate negative impact on memory and storage sector stocks. Major hardware suppliers including Micron Technology (MU), Western Digital (WDC), Seagate Technology (STX), and SanDisk (SNDK) all experienced significant declines as investors reacted to the potential for reduced memory chip demand.

Cloudflare CEO Matthew Prince commented: 'This is Google's DeepSeek. So much more room to optimize AI inference for speed, memory usage, power consumption, and multi-tenant utilization.' The development has drawn comparisons to the fictional 'Pied Piper' compression algorithm from HBO's Silicon Valley, highlighting its perceived disruptive potential. Google research scientists Amir Zandieh and Vahab Mirrokni noted that 'as AI becomes more integrated into all products, this work in fundamental vector quantization will be more critical than ever.'