# HetSeq: Distributed GPU Training on Heterogeneous Infrastructure

arXiv: https://arxiv.org/abs/2009.14783 · HTML: https://mlsanity.com/paper/2009.14783/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training](https://mlsanity.com/paper/2312.11819/) (2023)
- [HetCCL: Accelerating LLM Training with Heterogeneous GPUs](https://mlsanity.com/paper/2601.22585/) (2026)
- [Biased Local SGD for Efficient Deep Learning on Heterogeneous Systems](https://mlsanity.com/paper/2508.08540/) (2025)

## Abstract (arXiv)

Modern deep learning systems like PyTorch and Tensorflow are able to train enormous models with billions (or trillions) of parameters on a distributed infrastructure. These systems require that the internal nodes have the same memory capacity and compute performance. Unfortunately, most organizations, especially universities, have a piecemeal approach to purchasing computer systems resulting in a heterogeneous infrastructure, which cannot be used to compute large models. The present work describes HetSeq, a software package adapted from the popular PyTorch package that provides the capability to train large neural network models on heterogeneous infrastructure. Experiments with transformer translation and BERT language model shows that HetSeq scales over heterogeneous systems. HetSeq can be easily extended to other models like image classification. Package with supported document is publicly available at https://github.com/yifding/hetseq.