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cs.DC2025
Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices
Fahao Chen, Jie Wan, Peng Li +2
Federated fine-tuning of Mixture-of-Experts (MoE)-based large language models (LLMs) is challenging due to their massive computational requirements and the resource constraints of…
cs.DC2025
SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models
Fahao Chen, Peng Li, Tom H. Luan +2
Speculative decoding has been shown as an effective way to accelerate Large Language Model (LLM) inference by using a Small Speculative Model (SSM) to generate candidate tokens in…
cs.DC2025
Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management
Liu Qianli, Hong Zicong, Chen Fahao +2
Serving large language models (LLMs) for massive users is challenged by the significant memory footprint of the transient state, known as the key-value (KV) cache, which scales wit…