1 citations · 1 across the 7 of their papers we have counts for
7 papers
One Threshold Is Not Enough: Prompt-Invariant Caching Schedules for Video Diffusion
Om Agrawal, Saurabh Agarwal, Aditya Akella
Video diffusion models remain expensive because denoising repeatedly evaluates a costly model backbone. Dynamic caching methods like TeaCache, EasyCache, and DiCache reduce this co…
Sangam: Efficiently Serving Diffusion LLMs with the AR Stack
Nitin Kedia, Saurabh Agarwal, Myungjin Lee +1
Diffusion language models (dLLMs) generate text by iteratively denoising a masked response and can commit multiple output positions per model invocation. Their bidirectional attent…
CUCo: An Agentic Framework for Compute and Communication Co-design
Yoga Sri Varshan Varadharajan, Bodun Hu, Saurabh Agarwal +1
Computation and communication in distributed LLM training and inference are traditionally optimized in isolation; expert-crafted systems such as DeepEP, FLUX, and TokenWeave show t…
Nalar: Workflow-Aware Management of Agentic Applications
Marco Laju, Donghyun Son, Saurabh Agarwal +4
LLM-driven agentic applications automate complex, multi-step tasks, but serving them efficiently remains difficult due to heterogeneous components, dynamic model-driven control flo…
Software-Defined Agentic Serving
Saurabh Agarwal, Marco Laju, Jayanth Srinivasa +2
As multi-agent LLM pipelines grow in complexity, existing serving paradigms fail to adapt to the dynamic serving conditions. We argue that agentic serving systems should be program…
SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling
Fanjiang Ye, Zepeng Zhao, Yi Mu +11
Diffusion models have recently achieved remarkable success in generative tasks (e.g., image and video generation), and the demand for high-quality content (e.g., 2K/4K videos) is r…
Harmonia: End-to-End RAG Serving Optimization
Saurabh Agarwal, Bodun Hu, Luis Pabon +3
Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipelines efficiently is challenging becau…