3 papers
cs.LG2026
Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement
Qianli Liu, Kaibin Guo, Zicong Hong +5
Expert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models. Its efficiency depends on the communication and computation latencies of the GPUs, w…
cs.CL2026
KVDrive: A Holistic Multi-Tier KV Cache Management System for Long-Context LLM Inference
Jian Lin, Jiazhi Mi, Zicong Hong +5
Supporting long-context LLMs is challenging due to the substantial memory demands of the key-value (KV) cache. Existing offloading systems store the full cache in host memory and s…
cs.DC2024
Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation
Fahao Chen, Peng Li, Zicong Hong +2
Mixture-of-Experts (MoE) is an emerging technique for scaling large models with sparse activation. MoE models are typically trained in a distributed manner with an expert paralleli…