5 papers
EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints
Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3
Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continuously generated data from sm…
MobileFineTuner: A Mobile-Native Framework for On-Device LLM Fine-Tuning in Real-World Embedded AI Applications
Jiaxiang Geng, Lunyu Zhao, Yiyi Lu +1
Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal signals sensed from users and the…
MoFa: A Unified Performance Modeling Framework for LLM Pretraining
Lu Zhao, Rong Shi, Shaoqing Zhang +14
The exponential growth in LLM scales, with parameters soaring from billions to trillions, has necessitated distributed pretraining across large clusters comprising thousands to ten…
MemFine: Memory-Aware Fine-Grained Scheduling for MoE Training
Lu Zhao, Rong Shi, Shaoqing Zhang +21
The training of large-scale Mixture of Experts (MoE) models faces a critical memory bottleneck due to severe load imbalance caused by dynamic token routing. This imbalance leads to…
Disaggregated Prefill and Decoding Inference System for Large Language Model Serving on Multi-Vendor GPUs
Xing Chen, Rong Shi, Lu Zhao +4
LLM-based applications have been widely used in various industries, but with the increasing of models size, an efficient large language model (LLM) inference system is an urgent pr…