AI RESEARCH
ZipServ: Fast and Memory-Efficient LLM Inference with Hardware-Aware Lossless Compression
arXiv CS.LG
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ArXi:2603.17435v1 Announce Type: cross Lossless model compression holds tremendous promise for alleviating the memory and bandwidth bottlenecks in bit-exact Large Language Model (LLM) serving. However, existing approaches often result in substantial inference slowdowns due to fundamental design mismatches with GPU architectures: at the kernel level, variable-length bitstreams produced by traditional entropy codecs break SIMT parallelism; at the system level, decoupled pipelines lead to redundant memory traffic.