KV Cache Paging
KV Cache Paging is a memory management technique for Large Language Model (LLM) inference that treats Key-Value (KV) cache blocks as discrete pages. By decoupling logical token sequences from physical GPU memory allocation, it enables non-contiguous storage of KV states, significantly reducing memory fragmentation and improving throughput for variable-length generation workloads.
Core Mechanism
- Block-based Allocation: Instead of allocating fixed-size buffers for maximum context length per request, the system divides KV cache into smaller blocks (pages).
- Non-contiguous Storage: As tokens are generated, new blocks are allocated from a global pool regardless of their physical proximity to previous blocks.
- VRAM Optimization: This approach addresses the critical challenge of efficiently serving LLMs at scale by maximizing GPU memory utilization, preventing waste from over-provisioning for worst-case context lengths.
Implementation & Impact
- Throughput Improvement: By minimizing memory fragmentation, Paged Attention allows for higher batch sizes and more efficient handling of concurrent requests with varying sequence lengths.
- Framework Adoption: Popular inference engines like vllm utilize this mechanism to achieve significant speedups in serving latency and throughput.
- Reference Material: See KV Cache and Paged Attention: Accelerating LLM Inference through VRAM Optimization for detailed analysis on VRAM optimization strategies.