5 papers
LOCAL: Enabling Learning On-device Contiguously for Agent LLMs
Xinxin Liu, Jiaxin Li, Zibo Wang +7
On-device LLM agents interact repeatedly with users on local hardware, producing private traces that are valuable for adaptation but should not be sent to a remote trainer. Ideally…
STAR: Decode-Phase Rescheduling for LLM Inference
Zhibin Wang, Zetao Hong, Xue Li +8
Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalance in the decode phase, particularly f…
SmartSwap: Swap-Based Memory Optimization for LLM Training under Varying Operator Sequences
Zibo Wang, Yuhang Zhou, Zhibin Wang +13
The increasing size of large language models (LLMs) has led to a surge in memory requirements during training, often exceeding the capacity of high-bandwidth memory (HBM). Swap-bas…
Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding
Zhibin Wang, Zhonghui Zhang, Yuhang Zhou +8
Recent advancements in Mixture of Experts (MoE) models have significantly increased their parameter scale as well as model performance. Extensive offloading techniques have been pr…
Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving
Zhibin Wang, Shipeng Li, Xue Li +7
Large language models have been widely deployed in various applications, encompassing both interactive online tasks and batched offline tasks. Given the burstiness and latency sens…