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

arXiv: https://arxiv.org/abs/2312.12391 · HTML: https://mlsanity.com/paper/2312.12391/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 80 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://mlsanity.com/paper/2101.06840/) (2021)
- [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://mlsanity.com/paper/2104.07857/) (2021)
- [Beyond Data and Model Parallelism for Deep Neural Networks](https://mlsanity.com/paper/1807.05358/) (2018)
- [Memory-Efficient Pipeline-Parallel DNN Training](https://mlsanity.com/paper/2006.09503/) (2020)
- [DAPPLE: A Pipelined Data Parallel Approach for Training Large Models](https://mlsanity.com/paper/2007.01045/) (2020)
- [PyTorch Distributed: Experiences on Accelerating Data Parallel Training](https://mlsanity.com/paper/2006.15704/) (2020)
- [Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads](https://mlsanity.com/paper/1901.05758/) (2019)
- [Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning](https://mlsanity.com/paper/2008.12260/) (2020)
- [Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads](https://mlsanity.com/paper/2008.09213/) (2020)
- [ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale](https://mlsanity.com/paper/2303.14006/) (2023)
- [Supporting Very Large Models using Automatic Dataflow Graph Partitioning](https://mlsanity.com/paper/1807.08887/) (2018)
- [Themis: Fair and Efficient GPU Cluster Scheduling](https://mlsanity.com/paper/1907.01484/) (2019)
- [Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters](https://mlsanity.com/paper/2109.01313/) (2021)
- [PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units](https://mlsanity.com/paper/1909.04548/) (2019)

## Built on by (most cited first, 23 of 23)

- [Optimizing RLHF Training for Large Language Models with Stage Fusion](https://mlsanity.com/paper/2409.13221/) (2024)
- [LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale](https://mlsanity.com/paper/2408.05499/) (2024)
- [RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving](https://mlsanity.com/paper/2503.14649/) (2025)
- [APEX: An Extensible and Dynamism-Aware Simulator for Automated Parallel Execution in LLM Serving](https://mlsanity.com/paper/2411.17651/) (2024)
- [mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs](https://mlsanity.com/paper/2312.02515/) (2023)
- [Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models](https://mlsanity.com/paper/2406.01698/) (2024)
- [LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure](https://mlsanity.com/paper/2602.23036/) (2026)
- [Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training](https://mlsanity.com/paper/2504.09307/) (2025)
- [Compass: SLO-aware Query Planner for Compound AI Serving at Scale](https://mlsanity.com/paper/2504.16397/) (2025)
- [Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation](https://mlsanity.com/paper/2504.17025/) (2025)
- [M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data](https://mlsanity.com/paper/2602.08564/) (2026)
- [Maya: Optimizing Deep Learning Training Workloads using GPU Runtime Emulation](https://mlsanity.com/paper/2503.20191/) (2025)
- [Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs](https://mlsanity.com/paper/2511.10480/) (2025)
- [PASCAL: A Phase-Aware Scheduling Algorithm for Serving Reasoning-based Large Language Models](https://mlsanity.com/paper/2602.11530/) (2026)
- [Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference](https://mlsanity.com/paper/2606.02430/) (2026)
- [Towards a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation](https://mlsanity.com/paper/2405.02969/) (2024)
- [Phantora: Maximizing Code Reuse in Simulation-based Machine Learning System Performance Estimation](https://mlsanity.com/paper/2505.01616/) (2025)
- [Photonic Fabric Platform for AI Accelerators](https://mlsanity.com/paper/2507.14000/) (2025)
- [Training Time Prediction for Mixed Precision-based Distributed Training](https://mlsanity.com/paper/2604.16145/) (2026)
- [A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM](https://mlsanity.com/paper/2605.15617/) (2026)
- [Simthesizer: An Agent-Driven Simulation Framework for LLM Serving Systems](https://mlsanity.com/paper/2608.24650/) (2026)
- [Parallelism Strategy Chaining for Fast Training Convergence](https://mlsanity.com/paper/2609.07236/) (2026)
- [ATTUNE: Agentic Throughput Tuning for LLM Training](https://mlsanity.com/paper/2610.17528/) (2026)

## Abstract (arXiv)

As large language models (LLMs) become widespread in various application domains, a critical challenge the AI community is facing is how to train these large AI models in a cost-effective manner. Existing LLM training plans typically employ a heuristic based parallel training strategy which is based on empirical observations rather than grounded upon a thorough examination of the search space of LLM parallelization. Such limitation renders existing systems to leave significant performance left on the table, wasting millions of dollars worth of training cost. This paper presents our profiling-driven simulator called vTrain, providing AI practitioners a fast yet accurate software framework to determine an efficient and cost-effective LLM training system configuration. We demonstrate vTrain's practicality through several case studies, e.g., effectively evaluating optimal training parallelization strategies that balances training time and its associated training cost, efficient multi-tenant GPU cluster schedulers targeting multiple LLM training jobs, and determining a compute-optimal LLM model architecture given a fixed compute budget.