KV Cache Optimization: PagedAttention, Prefix Caching & Memory Management

The document explores the challenges and solutions associated with memory management in large language models, particularly focusing on the inefficiencies caused by the KV cache, which stores key and value tensors for reuse during text generation. The text highlights that traditional systems often suffer from significant memory waste due to fragmentation and over-allocation, necessitating roughly 640GB of KV cache for a 70B model with an 8K context and a batch size of 32, often surpassing the memory required for the model weights themselves. To optimize memory usage and improve throughput, the text discusses several advanced techniques such as PagedAttention, which reduces memory fragmentation by breaking the KV cache into smaller blocks, Automatic Prefix Caching (APC), which skips redundant computations by reusing previously computed shared segments, and KV cache quantization, which halves memory requirements by using FP8 precision. These optimizations are integrated into vLLM, a modern inference system, and are further supported by strategies like Grouped Query Attention (GQA) and cache offloading for extreme context lengths. Additionally, the text covers practical implementation checklists for deploying these optimizations and suggests that adopting these techniques can significantly enhance performance without requiring exotic hardware.

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