Google's TurboQuant Breakthrough Cuts AI Memory Usage by 6X With Zero Accuracy Loss. The Algorithm Could Slash Data Center Costs and Speed Up Every AI Model.
TurboQuant solves the memory bottleneck in AI inference by compressing Key-Value caches from 16 bits to just 3 bits while maintaining 100% accuracy and delivering 8x speedups.
Google DeepMind researchers have solved one of the biggest bottlenecks in AI inference with TurboQuant, an algorithm that compresses the memory requirements of large language models by six times without losing any accuracy. The breakthrough, presented at ICLR 2026, could dramatically reduce the cost of running AI models and make advanced AI accessible on consumer hardware.
The problem TurboQuant solves is massive. As AI models grow larger and handle longer conversations, they create enormous "Key-Value caches" that store previous parts of the conversation in memory. These caches can consume hundreds of gigabytes of expensive GPU memory, making it prohibitively expensive to run large models or handle long conversations.
The Memory Wall Is Real
Current frontier models like GPT-5 and Claude Opus can require up to 80GB of memory just to store the Key-Value cache for a single long conversation. Data centers running these models spend more on memory than on compute, creating a "memory wall" that limits how large models can get and how many users they can serve simultaneously.