Lynx: Progressive Speculative Quantization for accelerating KV Transfer in Long-Context Inference
Long-context inference is increasingly common in large language model (LLM) serving, driven by retrieval-augmented generation and agentic systems. In disaggregated inference, these workloads require transferring large Key-Value (KV) caches across the network, where decoding cannot begin until the transfer completes. Recent KV quantization techniques reduce data volume and alleviate this bottleneck, but existing schemes fail to achieve both low network-exposed latency and high inference accuracy. We challenge the assumption that the KV cache is an indivisible unit that must be fully received before use. We leverage the observation that different bits in the KV cache contribute unequally to...
| Authors | Wenchen Han, Gingfung Matthew Yeung, Marco Barletta et al. |
|---|---|
| Primary category | cs.DC |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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