15 citations · 24 across the 6 of their papers we have counts for
6 papers
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…
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…
On Evaluating Performance of LLM Inference Serving Systems
Amey Agrawal, Nitin Kedia, Anmol Agarwal +5
The rapid evolution of Large Language Model (LLM) inference systems has yielded significant efficiency improvements. However, our systematic analysis reveals that current evaluatio…
Etalon: Holistic Performance Evaluation Framework for LLM Inference Systems
Amey Agrawal, Anmol Agarwal, Nitin Kedia +5
Serving large language models (LLMs) in production can incur substantial costs, which has prompted recent advances in inference system optimizations. Today, these systems are evalu…
Vidur: A Large-Scale Simulation Framework For LLM Inference
Amey Agrawal, Nitin Kedia, Jayashree Mohan +5
Optimizing the deployment of Large language models (LLMs) is expensive today since it requires experimentally running an application workload against an LLM implementation while ex…
Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve
Amey Agrawal, Nitin Kedia, Ashish Panwar +5
Each LLM serving request goes through two phases. The first is prefill which processes the entire input prompt and produces the first output token and the second is decode which ge…