5 papers
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…
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…
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…
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…
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…