DeltaKV: Residual-Based KV Cache Compression via Long-Range Similarity

A residual KV cache compression framework that exploits long-range inter-token similarity and shared latent components. Long context is increasingly bottlenecked by cache growth, and residual structure is another way to reduce memory movement.

DeltaKV is relevant to local builders because context growth can turn a fast GPU into a memory-bound system.

Long context is increasingly bottlenecked by cache growth, and residual structure is another way to reduce memory movement. 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.

For agent and long-document workloads, cache compression quality may matter more than raw single-token decode speed. 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.