KV Cache Optimization: PagedAttention, Prefix Caching & Memory Management

Memory management in large language models (LLMs) is a critical aspect, especially when dealing with the memory-intensive KV cache used during text generation. The dependency on previous tokens creates a linear memory problem that can exceed the model weights themselves, as seen with a 70B model requiring 640GB of KV cache for processing an 8K context with a batch size of 32. Optimization techniques such as PagedAttention, prefix caching, and FP8 quantization significantly enhance memory efficiency and computational performance. PagedAttention reduces memory waste by breaking the KV cache into small, non-contiguous blocks, reducing waste from 60-80% to under 4%, translating into 2-4x throughput improvements. Automatic Prefix Caching reuses KV tensors for shared segments, reducing redundant computations in scenarios like long document queries and multi-turn conversations. FP8 quantization halves memory requirements and maintains accuracy by storing tensors in an 8-bit format. Additional strategies like Grouped Query Attention (GQA) and cache offloading provide further optimizations, with GQA reducing KV cache size by sharing key-value heads across query heads, while cache offloading shifts the cache to CPU or SSD when GPU memory is insufficient. These techniques, supported by tools like vLLM and platforms such as Prem Studio, offer measurable gains without requiring exotic hardware or complex configurations, allowing teams to focus on model quality rather than inference engineering.