PackKV: Efficient KV Cache Compression for Long-Context LLMs
Analysis
This paper addresses the memory bottleneck of long-context inference in large language models (LLMs) by introducing PackKV, a KV cache management framework. The core contribution lies in its novel lossy compression techniques specifically designed for KV cache data, achieving significant memory reduction while maintaining high computational efficiency and accuracy. The paper's focus on both latency and throughput optimization, along with its empirical validation, makes it a valuable contribution to the field.
Key Takeaways
- •Proposes PackKV, a KV cache management framework for long-context LLMs.
- •Introduces lossy compression techniques tailored for KV cache data.
- •Achieves significant memory reduction (up to 179.6% for V cache) with minimal accuracy drop.
- •Optimizes for both latency and throughput, improving matrix-vector multiplication performance.
- •Demonstrates performance gains on A100 and RTX Pro 6000 GPUs.
Reference
“PackKV achieves, on average, 153.2% higher memory reduction rate for the K cache and 179.6% for the V cache, while maintaining accuracy.”