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cs.DC2025
MorphServe: Efficient and Workload-Aware LLM Serving via Runtime Quantized Layer Swapping and KV Cache Resizing
Zhaoyuan Su, Zeyu Zhang, Tingfeng Lan +4
Efficiently serving large language models (LLMs) under dynamic and bursty workloads remains a key challenge for real-world deployment. Existing serving frameworks and static model…
cs.DC2025
ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates
Tingfeng Lan, Yusen Wu, Bin Ma +7
Fine-tuning large language models (LLMs) often exceeds GPU memory limits, prompting systems to offload model states to CPU memory. However, existing offloaded training frameworks l…
cs.DC2025
λScale: Enabling Fast Scaling for Serverless Large Language Model Inference
Minchen Yu, Rui Yang, Chaobo Jia +9
Serverless computing has emerged as a compelling solution for cloud-based model inference. However, as modern large language models (LLMs) continue to grow in size, existing server…