Serving offline large language model (LLM) inference workloads (e.g., log summarization and bulk translation) can consume up to 30% of GPUs in production. Despite this significant share, the characteristics of offline inference remain largely understudied. In this paper, we start by analyzing 1.5 million tasks comprisi...
Le-Ping Yang, Xue Li, Kun Qian et al.· Proceedings of the ACM SIGOP...· 0 citations
DSpark-style parallel drafters have made speculative decoding highly effective, yet their draft phase remains serialized on the critical path of every round. Parallel speculative decoding (PSD) overlaps drafting with verification, yet existing methods must guess the accepted prefix and bonus token in advance: a wrong g...
Fu-Liang Liu, Xue Li, Kun Qian et al.· 0 citations
SMetric addresses session-centric scheduling with differential scheduling based on two indicators derived from the request itself, the session turn and the local KV\$ hit: it schedules first-turn requests for load balance, and sticks follow-ups to the instance with the highest local hit for high KV\$ reuse.
