Efficient large-scale language model training on GPU clusters using megatron-LM
Open accessAbstract
Large language models have led to state-of-the-art accuracies across several tasks. However, training these models efficiently is challenging because: a) GPU memory capacity is limited, making it impossible to fit large models on even a multi-GPU server, and b) the number of compute operations required can result in unrealistically long training times. Consequently, new methods of model parallelism such as tensor and pipeline parallelism have been proposed. Unfortunately, naive usage of these methods leads to scaling issues at thousands of GPUs. In this paper, we show how tensor, pipeline, and data parallelism can be composed to scale to thousands of GPUs. We propose a novel interleaved pipelining schedule that can improve throughput by 10+% with memory footprint comparable to existing approaches. Our approach allows us to perform training iterations on a model with 1 trillion parameters at 502 petaFLOP/s on 3072 GPUs (per-GPU throughput of 52% of theoretical peak).
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Current pipeline parallelism implementations have constraints on supported batch size per GPU, requiring it to be divisible by the number of pipeline stages.
“Current implementations have constraints on supported batch size per GPU, requiring it to be divisible by the number of pipeline stages.”
From The Llama 3 Herd of Models (2024) · cites this paper as Narayanan et al., 2021
Section 2.2.2 (page not available) says the paper’s interleaved schedule requires the number of microbatches in a batch to be a multiple of the number of devices in the pipeline. It does not state that batch size per GPU must be divisible by the number of pipeline stages for pipeline-parallel implementations generally; the stated requirement applies to this particular schedule and to microbatch count. (LLM, conf=0.86)
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Federated and edge learning for large language models
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Calculon: a methodology and tool for high-level co-design of systems and large language models
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An Efficient 2D Method for Training Super-Large Deep Learning Models
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A Survey on Auto-Parallelism of Large-Scale Deep Learning Training
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Sustainable Supercomputing for AI
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Distributed Training of Large Language Models
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What Language Model to Train if You Have One Million GPU Hours?
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A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads
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Mobius: Fine Tuning Large-Scale Models on Commodity GPU Servers
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Language models for materials discovery and sustainability: Progress, challenges, and opportunities
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AdaPipe: Optimizing Pipeline Parallelism with Adaptive Recomputation and Partitioning
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When the Edge Meets Transformers: Distributed Inference with Transformer Models
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Characterizing Multi-Instance GPU for Machine Learning Workloads
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Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real System
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RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
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Language models for the prediction of SARS-CoV-2 inhibitors
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ScheMoE: An Extensible Mixture-of-Experts Distributed Training System with Tasks Scheduling
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- Authors
- Narayanan, D., Shoeybi, M., Casper, J., LeGresley, P., Patwary, M., Korthikanti, V., Vainbrand, D., Kashinkunti, P., Bernauer, J., Catanzaro, B., Phanishayee, A., & Zaharia, M.
- Affiliations
- NVIDIA and Microsoft Research
- Nvidia (United Kingdom) (GB)
- Microsoft (United States) (US)
- Stanford University (US)
- ORCID
- Venue
- Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
- Publisher
- ACM
- Published
- 2021-11-13
- Pages
- 1-15
- ISSN
- 2167-4337 (ISSN-L)
- OpenAlex
- W3187255235
- Cited by
- 639
- References
- 39
- Citations by year
- 2026: 151 · 2025: 211 · 2024: 152 · 2023: 90 · 2022: 33 · 2021: 2
- FWCI
- 50.34
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference for High Performance Computing, Networking, Storage and Analysis (SC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (DGE-165651 8, 1651570, 1656518, CAREER)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language model training, GPU clusters, tensor parallelism, pipeline parallelism, data parallelism, memory efficiency, training throughput
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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