ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference

Understand

With the widespread deployment of long-context large language models (LLMs), there has been a growing demand for efficient support of high-throughput inference.

• However, as the key-value (KV) cache expands with the sequence length, the increasing memory footprint and the need to access it for each token generation both result in low throughput when serving long-context LLMs.
• While various dynamic sparse attention methods have been proposed to speed up inference while maintaining generation quality, they either fail to sufficiently reduce GPU memory consumption or introduce significant decoding latency by offloading the KV cache to the CPU.
• We present ShadowKV, a high-throughput long-context LLM inference system that stores the low-rank key cache and offloads the value cache to reduce the memory footprint for larger batch sizes and longer sequences.