KV Cache Optimization Strategies for Scalable and Efficient LLM Inference

A survey that organizes KV cache optimization into eviction, compression, hybrid memory, novel attention, and combined strategies. It is a useful map of the current KV-cache field, which has become the core bottleneck for long-context inference.

This is the map page behind GPU Hunter's long-context recommendations: more VRAM helps, but cache policy decides whether that VRAM is enough.

It is a useful map of the current KV-cache field, which has become the core bottleneck for long-context inference. The useful part for GPU Hunter readers is not the abstract result alone; it is the hardware implication: whether a model fits, whether a runtime can use the format, or whether throughput is limited by memory movement instead of arithmetic.

Use this to decide whether a workload needs cache compression, offload, eviction, or a hybrid policy before buying more VRAM. For long-context work, KV cache behavior is often the constraint that shows up after the model weights already fit. Cache precision, eviction, reuse, and memory movement can change the practical value of the same GPU.