PolarQuant-KV: A New Scheme for Extreme Compression of LLM KV Cache on Consumer GPUs

PolarQuant-KV achieves a 73-99% compression rate for KV cache on consumer GPUs using innovative polar coordinate quantization technology, while maintaining zero token loss, bringing a revolutionary breakthrough to local large model deployment. This scheme addresses the KV cache bottleneck in LLM inference, balances compression efficiency and generation quality, and represents an important advancement in the field of inference optimization.

The inference efficiency of large language models (LLMs) restricts their popularization on consumer hardware, and KV cache memory usage has become a core bottleneck. In long-context tasks, KV cache memory even exceeds model weights, making it difficult for ordinary consumer GPUs to run large models. Traditional compression methods need to balance compression rate and generation quality, which easily leads to information loss or token errors. How to reduce KV cache usage while maintaining performance is an industry challenge.

PolarQuant-KV proposes a polar coordinate quantization method, converting KV vectors from Cartesian coordinates to polar coordinates for processing, leveraging the directional characteristics of vectors in the attention mechanism. Observations show that the angle component contains more semantic information, so an asymmetric strategy is adopted for K/V cache: high-precision encoding for the angle component and aggressive compression for the magnitude component, achieving high compression rate while maintaining the accuracy of attention calculation.

Core innovations include: 1. Adaptive grouping quantization, dynamically adjusting group size to match data distribution; 2. Hierarchical quantization strategy, applying differentiated compression parameters to local/global attention heads; 3. Zero token loss guarantee, avoiding cumulative errors through quantization-dequantization processes and error compensation mechanisms; 4. Seamless integration with mainstream inference frameworks (vLLM, TensorRT-LLM, llama.cpp) without modifying the model or retraining.

Benchmark tests show that a 73-99% compression rate is achieved on Llama2/3 series models; with a 4:1 compression ratio, perplexity decreases by <0.5%, and dialogue quality is almost unchanged. Consumer GPUs (e.g., RTX4090 24GB) can run 70B models that were previously impossible to deploy, with KV cache compressed from tens of GB to a few GB. In terms of inference speed, throughput is improved in some configurations, and the impact of quantization overhead on latency is <5%.

Application values include: lowering the threshold for local experiments for individual developers; reducing inference hardware costs for enterprises and improving infrastructure utilization; significant advantages in long-context scenarios such as long document processing, code assistance, and multi-turn dialogue; the core idea can be migrated to inference optimization for mobile/embedded devices.

Current limitations: Benefit differences across different model architectures, and the performance of GQA attention variants needs optimization; single-card inference is the main focus, and multi-card parallel strategies need to be explored. Future directions: Combining with speculative decoding to improve throughput; combining with dynamic KV cache management to adaptively adjust compression strategies; further expanding to adapt to more models and scenarios.

PolarQuant-KV achieves extreme compression of KV cache through polar coordinate quantization without sacrificing generation quality, providing a practical solution for local large model deployment and reducing enterprise inference costs. As LLM applications expand, such underlying optimization technologies will play a key role in promoting the popularization of LLMs.