The KV cache functions by storing intermediate representations of previous tokens, facilitating efficient model responses without the need to recalculate earlier tokens. However, as the context windows expand, the corresponding memory requirements can escalate rapidly, presenting a challenge for even the most robust systems. TurboQuant addresses this challenge by applying compression techniques at very low precision while ensuring that the core mathematical properties of the attention mechanisms remain intact. Notably, TurboQuant can potentially decrease memory usage for the KV cache by up to six times, compressing data representation down to only a few bits per value. This is achieved without the necessity for model retraining or fine-tuning, allowing implementation directly during inference with minimal accuracy loss. This combination of high compression rates and the absence of retraining is particularly valuable for organizations seeking efficiency without significant system overhauls. While compression techniques are not a novel idea and quantization has been used to minimize model weights, KV cache compression presents its own complexities due to the high-dimensional data structures involved. TurboQuant utilizes a two-tiered approach: first, it transforms vectors into a more compressible format at low precision through a method named PolarQuant. Secondly, a lightweight correction mechanism ensures that distortions introduced during this compression do not negatively impact the model’s outputs. The practical benefits of TurboQuant extend beyond mere model efficiency.