Beyond Compression Ratio: High-Throughput KV Compression for Disaggregated LLM Serving
Junhong Wu, Zedong Liu, Xinyang Ma, Yuhan Chen, Guangming Tan, Dingwen Tao
KV cache compression reduces the communication volume of disaggregated large language model serving, but its practical benefit depends not only on compression ratio and model quality, but also on how efficiently the compression pipeline executes. This requirement becomes increasingly important as modern serving systems adopt high-bandwidth interconnects, where inefficient compression can offset a substantial portion of the saved communication time. This paper presents FastKVComp, a high-throughput KV cache compression data path built on top of KVServe. FastKVComp improves compression execution in two complementary ways. First, it fuses the distribution transformation and low-bit quantization stages into a KVaware TileLang kernel, eliminating intermediate tensor materialization and redundant memory accesses. Second, it replaces the original lossless compression backend with an open, extensible, and searchable backend based on LC, enabling high-performance compression operator pipelines. The fused kernel achieves up to 5.9x speedup, while the new lossless backend increases compression throughput from below 90 GB/s to over 500 GB/s. Across the evaluated workloads, FastKVComp reduces job completion time by up to 2.04x. End-to-end evaluation shows that these optimizations allow KV compression to retain meaningful communication benefits under modern high-bandwidth network.
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