1 citations · 1 across the 7 of their papers we have counts for
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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: An agent serving framework
Marco Laju, Donghyun Son, Saurabh Agarwal +4
LLM-driven agentic applications increasingly automate complex, multi-step tasks, but serving them efficiently remains challenging due to heterogeneous components, dynamic and model…
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…
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…