In the reasoning process of large language models (LLMs), memory bottlenecks have always been the "number one killer" restricting performance. Every time AI processes long texts or generates complex answers, a "working memory" called KV cache (Key-Value Cache) rapidly expands, causing the system to slow down or even crash. To address this challenge, Google Research officially launched a new AI memory compression technology called TurboQuant on March 26, 2026.

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The core breakthrough of this technology is that it can reduce the cache memory usage to one sixth of the original without sacrificing model accuracy, while achieving an impressive eightfold increase in inference speed.

Overcoming the KV Cache Bottleneck: Let AI Remember More and Run Faster

The emergence of TurboQuant marks a new dimension in AI operational efficiency. It adopts an advanced vector quantization scheme, mainly consisting of the PolarQuant quantization method and QJL optimization approach. In rigorous tests on mainstream open-source large models such as Gemma and Mistral, TurboQuant demonstrated strong adaptability: it can efficiently compress key-value caches to 3 bits without any pre-training or fine-tuning. In the "needle in a haystack" long context test simulating real and complex scenarios, the technology achieved zero precision loss, meaning that after significantly reducing its size, AI can still maintain its original intelligence and memory accuracy.

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Hardware Efficiency Peak: An 8-Fold Jump on H100 Accelerators

Aside from reducing memory usage, TurboQuant also impresses in hardware utilization. On high-performance H100 GPU accelerators, the TurboQuant optimized to 4 bits runs 8 times faster than the unquantized 32-bit baseline.

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