6 papers
Pin Once, Swap Light: Subspace-Aligned Centroid-Residual Training for Efficient Ultra-LoRA Serving
Xiang Li, Pengcheng Wang, Huazheng Wang +1
Modern multi-tenant Low-Rank Adapters (LoRAs) serving systems concurrently host tens to hundreds of LoRA adapters. Though powerful, this introduces a critical system dilemma betwee…
Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs
Pengcheng Wang, Zhiquan Wang, Jayoung Lee +5
Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across vision-language tasks. However, their high inference cost, arising from both the large…
CAMILA: Context-Aware Masking for Image Editing with Language Alignment
Hyunseung Kim, Chiho Choi, Srikanth Malla +3
Text-guided image editing has been allowing users to transform and synthesize images through natural language instructions, offering considerable flexibility. However, most existin…
Learning to Inference Adaptively for Multimodal Large Language Models
Zhuoyan Xu, Khoi Duc Nguyen, Preeti Mukherjee +4
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in visual reasoning, yet come with substantial computational cost, limiting their deployment in resource…
Ascendra: Dynamic Request Prioritization for Efficient LLM Serving
Azam Ikram, Xiang Li, Sameh Elnikety +1
The rapid advancement of Large Language Models (LLMs) has driven the need for more efficient serving strategies. In this context, efficiency refers to the proportion of requests th…
Hubs and Spokes Learning: Efficient and Scalable Collaborative Machine Learning
Atul Sharma, Kavindu Herath, Saurabh Bagchi +2
We introduce the Hubs and Spokes Learning (HSL) framework, a novel paradigm for collaborative machine learning that combines the strengths of Federated Learning (FL) and Decentrali…