34 citations · 37 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks
Fahao Chen, Peng Li, Celimuge Wu
Dynamic Graph Neural Network (DGNN) has shown a strong capability of learning dynamic graphs by exploiting both spatial and temporal features. Although DGNN has recently received c…