Google might have solved RAMageddon with TurboQuant
Google Research developed TurboQuant, a new AI memory compression algorithm designed to enhance efficiency in artificial intelligence systems.
The algorithm allows AI models to process more information with less memory, potentially reducing operational costs for AI inference, prompting comparisons to the fictional compression technology depicted in HBO’s “Silicon Valley.”
Google Research described TurboQuant as a novel method to shrink AI’s working memory, known as the KV cache, without affecting performance. The company said this compression could reduce runtime memory by at least six times.
The compression method uses vector quantization to clear cache bottlenecks in AI processing. This enables AI systems to retain accuracy while using less space.
Video: Google
The researchers plan to present their findings at the ICLR 2026 conference next month. They will detail two methods facilitating this compression: the PolarQuant quantization method and the QJL training and optimization method.
Cloudflare CEO Matthew Prince compared TurboQuant to Google’s “DeepSeek moment,” referencing efficiency gains from an AI model that achieved competitive results using fewer resources.
TurboQuant has not yet seen broad deployment and remains a laboratory breakthrough at this stage. It targets inference memory, not training memory, which continues to demand substantial RAM.