16 citations · 43 across the 4 of their papers we have counts for
4 papers
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…
SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills
Amey Agrawal, Ashish Panwar, Jayashree Mohan +3
Large Language Model (LLM) inference consists of two distinct phases - prefill phase which processes the input prompt and decode phase which generates output tokens autoregressivel…
Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads
Dharma Shukla, Muthian Sivathanu, Srinidhi Viswanatha +23
Lowering costs by driving high utilization across deep learning workloads is a crucial lever for cloud providers. We present Singularity, Microsoft's globally distributed schedulin…