# MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs

arXiv: https://arxiv.org/abs/2301.00407 · HTML: https://mlsanity.com/paper/2301.00407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 1 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [A Review on Deep Learning Techniques for Video Prediction](https://mlsanity.com/paper/2004.05214/) (2020)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [AI Benchmark: All About Deep Learning on Smartphones in 2019](https://mlsanity.com/paper/1910.06663/) (2019)
- [M2E-Try On Net: Fashion from Model to Everyone](https://mlsanity.com/paper/1811.08599/) (2018)
- [Fathom: Reference Workloads for Modern Deep Learning Methods](https://mlsanity.com/paper/1608.06581/) (2016)
- [Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications](https://mlsanity.com/paper/1902.04610/) (2019)
- [Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem](https://mlsanity.com/paper/2109.11067/) (2021)
- [Look, Read and Feel: Benchmarking Ads Understanding with Multimodal Multitask Learning](https://mlsanity.com/paper/1912.10248/) (2019)

## Built on by (most cited first, 1 of 1)

- [Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs](https://mlsanity.com/paper/2606.30391/) (2026)

## Abstract (arXiv)

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.