KV Cache Is Eating Your VRAM. Here’s How Google Fixed It With TurboQuant.

Towards Data Science / 4/19/2026

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Key Points

TurboQuant is presented as an end-to-end KV cache quantization framework designed to significantly reduce VRAM usage during inference.
The approach uses multi-stage compression, including PolarQuant and QJL residuals, to target near-lossless storage of KV caches.
By minimizing memory overhead, TurboQuant makes it feasible to use massive context windows without proportionally increasing GPU memory requirements.
The article focuses on the technical pipeline and how its components work together to improve KV cache efficiency.
Overall, TurboQuant is positioned as a practical method to address KV-cache memory bottlenecks in long-context scenarios.

Explore the end-to-end pipeline of TurboQuant, a novel KV cache quantization framework. This overview breaks down how multi-stage compression achieves near-lossless storage through PolarQuant and QJL residuals, enabling massive context windows with minimal memory overhead

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