QAQ: Advancing LLM Performance with KV Cache Quantization

The deployment hurdle of ballooning Key-Value (KV) cache sizes in LLMs draws a solution from QAQ, a Quality Adaptive Quantization strategy. QAQ’s theoretical underpinnings inform separate quantization strategies for the key and value caches, vastly improving the handling of larger contexts with minimal impact on model performance.

• Performance Bottlenecks: Tackling the expansion of KV cache with longer contexts
• Distinct Strategies: Key cache and value cache quantized differently due to their unique sensitivities
• Outlier Handling: Incorporates outlier considerations for comprehensive quantization
• Compression Achievements: Up to 10x compression with negligible performance reduction

QAQ provides a promising route to deploy LLMs more efficiently, especially for applications that necessitate longer contextual understanding.