This survey paper examines KV cache management techniques for accelerating LLM inference, categorizing optimizations into token-level, model-level, and system-level approaches.
KV cache in LLMs faces critical challenges with memory usage and computational overhead during inference, especially for long sequences, making real-world deployment difficult.
https://arxiv.org/abs/2412.19442v2
🔧 Methods explored in this Paper:
→ Introduces a taxonomy of KV cache management strategies across three levels: token, model, and system
→ Token-level optimizations focus on selecting, allocating, merging, quantizing, and decomposing KV pairs without architectural changes
→ Model-level approaches redesign attention mechanisms and architectures for efficient KV reuse
→ System-level solutions optimize memory management and scheduling across different computing environments. System-level approaches improve throughput by 2-3x
→ The survey analyzes trade-offs between memory efficiency and model performance for each approach
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💡 Key Insights:
→ KV cache size grows linearly with sequence length and layer count, making efficient management crucial
→ Different optimization levels can be combined for better results
→ Token-level methods are easiest to implement but have limited gains, achieve 2-4x memory reduction
→ Model-level changes offer better efficiency but require retraining. reduce KV cache size by up to 8x
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