# vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training

**Authors:** Jehyeon Bang, Yujeong Choi, Myeongwoo Kim, Yongdeok Kim, Minsoo Rhu  
**arXiv:** [2312.12391](https://arxiv.org/abs/2312.12391)  
**Date:** 2023-11-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2312.12391v1/

## Key points

- Large language models (LLMs) are widely used but pose a challenge in terms of cost-effective training methods.
- Traditional LLM training strategies rely on heuristic-based parallel training approaches, leading to suboptimal performance and high training costs.
- The paper introduces vTrain, a profiling-driven simulator to help AI practitioners find efficient and cost-effective configurations for LLM training.
- vTrain allows users to evaluate different parallelization strategies to balance reducing training time and minimizing costs.
- The simulator aids in developing multi-tenant GPU cluster schedulers for handling multiple LLM training jobs concurrently.
- Users can identify compute-optimal LLM model architectures within predefined budget constraints using vTrain.
- Through case studies, vTrain showcases its effectiveness in optimizing parallelization strategies and designing compute-efficient model architectures for large language model training.

## Summary

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Large language models (LLMs) are increasingly being utilized across various application domains, presenting a significant challenge for the AI community in terms of cost-effective training methods. Traditionally, LLM training strategies have relied on heuristic-based parallel training approaches, lacking a comprehensive exploration of the potential optimization opportunities within the parallelization process. This oversight results in suboptimal performance and substantial wastage of financial resources amounting to millions of dollars in training costs. To address this issue, this paper introduces vTrain, a profiling-driven simulator designed to assist AI practitioners in determining efficient and cost-effective configurations for training large language models. By leveraging vTrain, practitioners can swiftly evaluate different parallelization strategies to strike a balance between reducing training time and minimizing associated costs. Additionally, the simulator facilitates the development of efficient multi-tenant GPU cluster schedulers capable of handling multiple LLM training jobs concurrently. Furthermore, vTrain enables users to identify compute-optimal LLM model architectures within predefined budget constraints. Through several case studies showcased in this paper, including evaluating optimal parallelization strategies and designing compute-efficient model architectures, vTrain demonstrates its practicality and effectiveness in enhancing the overall efficiency of large language model training processes. Authored by Jehyeon Bang, Yujeong Choi, Myeongwoo Kim, Yongdeok Kim, and Minsoo Rhu, "vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training" presents a valuable tool for AI researchers and practitioners seeking to optimize their LLM training systems while maximizing resource utilization and minimizing costs.