10 papers · 1 filter
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
Jiahao Wang, Kaizhan Lin, Kaixi Zhang +7
LLM scheduling is critical to serving, yet it remains unclear how well existing designs fit agentic serving--with LLM requests issued by agents instead of humans. This shifts the w…
KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider
Jiahao Wang, Jinbo Han, Xingda Wei +6
Serving large language models (LLMs) is important for cloud providers, and caching intermediate results (KV$) after processing each request substantially improves serving throughp…
Fast LLM Post-training via Decoupled and Fastest-of-N Speculation
Rongxin Cheng, Kai Zhou, Xingda Wei +8
Rollout dominates the training time in large language model (LLM) post-training, where the trained model is used to generate tokens given a batch of prompts. This work, SpecActor,…
KunServe: Parameter-centric Memory Management for Efficient Memory Overloading Handling in LLM Serving
Rongxin Cheng, Yuxin Lai, Xingda Wei +2
Serving LLMs with a cluster of GPUs is common nowadays, where the serving system must meet strict latency SLOs required by applications. However, the stateful nature of LLM serving…
PhoenixOS: Concurrent OS-level GPU Checkpoint and Restore with Validated Speculation
Xingda Wei, Zhuobin Huang, Tianle Sun +5
PHOENIXOS (PHOS) is the first OS service that can concurrently checkpoint and restore (C/R) GPU processes--a fundamental capability for critical tasks such as fault tolerance, proc…
Towards Lock Modularization for Heterogeneous Environments
Hanze Zhang, Rong Chen, Haibo Chen
Modern hardware environments are becoming increasingly heterogeneous, leading to the emergence of applications specifically designed to exploit this heterogeneity. Efficiently adop…