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25 changes: 22 additions & 3 deletions README.md
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# ConsumerBench
# ConsumerBench: Benchmarking Generative AI Applications on End-User Devices

## 📑 Overview
<div align="center">

ConsumerBench is a comprehensive benchmarking framework that evaluates the runtime performance of user-defined GenAI applications under realistic conditions on end-user devices.
**ConsumerBench is a comprehensive benchmarking framework that evaluates the runtime performance of user-defined GenAI applications under realistic conditions on end-user devices.**

[![Website](https://img.shields.io/badge/Website-consumerbench.github.io-blue?logo=google-chrome)](https://consumerbench.github.io/)
[![arXiv](https://img.shields.io/badge/arXiv-2506.17538-red?logo=arxiv)](https://arxiv.org/abs/2506.17538)
[![License](https://img.shields.io/badge/License-MIT-green)](./LICENSE)

</div>

## 🚀 Benchmark Setup

Expand Down Expand Up @@ -121,3 +127,16 @@ To modify Service Level Objectives (SLOs):
### End-to-End User Workflow
- **Greedy allocation:** [`configs/workflow_content_creation.yml`](configs/workflow_content_creation.yml)
- **GPU partitioning:** [`configs/workflow_content_creation_mps.yml`](configs/workflow_content_creation_mps.yml)

## 🌟 Citation

If you use ConsumerBench in your research, please cite:

```bibtex
@article{gu2025consumerbench,
title={ConsumerBench: Benchmarking Generative AI Applications on End-User Devices},
author={Gu, Yile and Kadekodi, Rohan and Nguyen, Hoang and Nguyen, Keisuke and Liu, Yiyu and Kasikci, Baris,
journal={arXiv preprint arXiv:2506.17538},
year={2025}
}
```