Google Cuts AI Memory Needs by 6x Without Losing Performance

Google Research has unveiled TurboQuant, a new compression algorithm that can reduce the memory requirements of large language models by up to six times and accelerate computations by eight times, without any loss of accuracy.

This is not a minor improvement. It is a potential paradigm shift for the entire AI industry.

The core of TurboQuant is optimization of the key-value KV cache, which stores intermediate vector representations to avoid recalculating previous tokens. By compressing cache precision from 16-bit to approximately 3-bit, memory bandwidth pressure is dramatically reduced and cache residency improves.

What makes TurboQuant particularly compelling is that it requires no retraining or fine-tuning. The algorithm can be integrated directly into existing inference pipelines, meaning existing models can immediately benefit from the improvement.

The implications are significant: AI systems can serve far more users per GPU, respond faster, and support longer context windows without requiring additional hardware. For the semiconductor industry, it could mean reduced demand for high-bandwidth memory.

Google will present a paper on TurboQuant at the ICLR conference in Rio de Janeiro in late April. The market reaction was immediate: Alphabet stock suffered its largest single-day drop since June 2025 following the news, because investors fear the algorithm reduces demand for the expensive memory chips driving the AI boom.