The KV Cache Survival Guide: Why Your GPU Runs Out of Memory with Local LLMs

KV cache management is one of the most pressing challenges for anyone running large language models locally. As sequence lengths grow and batch sizes increase, the memory footprint of attention key-value tensors can quickly exhaust even high-end GPUs, forcing practitioners to reduce context windows or batch sizes at the cost of throughput and quality.

This guide explores why KV cache becomes such a bottleneck during inference, examining the memory requirements across different model sizes and sequence lengths. It covers practical mitigation techniques including attention variants (like Flash Attention), dynamic quantisation of cache tensors, and strategic batching approaches that allow you to squeeze more performance from limited VRAM.

For local LLM deployments, understanding KV cache dynamics is essential for balancing quality, speed, and hardware constraints. Whether you're optimising for consumer GPUs or edge devices, this survival guide provides the technical depth needed to make informed architectural decisions.

Source: SitePoint · Relevance: 10/10