Sarathi-Serve

This is the official OSDI'24 artifact submission for paper #444, "Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve”.

Setup

Setup CUDA

Sarathi-Serve has been tested with CUDA 12.1 on A100 and A40 GPUs.

Clone repository

git clone https://[email protected]/msri/AI-Infrastructure/_git/llm-batching

Create mamba environment

Setup mamba if you don't already have it,

wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh
bash Mambaforge-Linux-x86_64.sh # follow the instructions from there

Create a Python 3.10 environment,

mamba create -p ./env python=3.10  

Install Sarathi-Serve

pip install -e . --extra-index-url https://flashinfer.ai/whl/cu121/torch2.3/

Reproducing Results

Refer to readmes in individual folders corresponding to each figure in osdi-experiments.

Citation

If you use our work, please consider citing our paper:

@article{agrawal2024taming,
  title={Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve},
  author={Agrawal, Amey and Kedia, Nitin and Panwar, Ashish and Mohan, Jayashree and Kwatra, Nipun and Gulavani, Bhargav S and Tumanov, Alexey and Ramjee, Ramachandran},
  journal={Proceedings of 18th USENIX Symposium on Operating Systems Design and Implementation, 2024, Santa Clara},
  year={2024}
}

Acknowledgment

This repository originally started as a fork of the vLLM project. Sarathi-Serve is a research prototype and does not have complete feature parity with open-source vLLM. We have only retained the most critical features and adopted the codebase for faster research iterations.