PagedAttention vs. RadixAttention: Optimizing LLM KV Cache Management

Modern LLMs rely on quantization, pruning, distillation, and faster attention kernels, but production performance often depends most on KV cache management. As context windows grow, the cache consumes significant GPU memory, limiting concurrency, throughput, and latency. Two breakthroughs transformed this challenge: PagedAttention improves memory allocation, while RadixAttention enables efficient prefix reuse. Together, these techniques make LLM serving faster and more memory-efficient. In this article, we examine how PagedAttention and RadixAttention work, why they matter, and how they enable high-performance LLM serving. PagedAttention vs. RadixAttention: What’s the Difference? Every transformer generates text one token at a time. For each new token, the model must attend to all previously generated tokens by using their key (K) and value (V) vectors. Recomputing these vectors at every step would make generation prohibitively expensive, so serving engines store them in memory as the KV cache. This cache eliminates redundant computation and makes autoregressive decoding practical, but it introduces a new challenge: memory consumption grows linearly with sequence length. For long-context models, the KV cache often becomes the largest dynamic consumer of GPU memory, determining how many requests can run simultaneously. The size of the KV cache depends on the model architecture and the number of…