List of NVIDIA DGX Platform Customers
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Since 2010, our global team of researchers has been studying NVIDIA DGX Platform customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased NVIDIA DGX Platform for AI infrastructure from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using NVIDIA DGX Platform for AI infrastructure include: BMW, a Germany based Automotive organisation with 157457 employees and revenues of $165.99 billion, Lockheed Martin, a United States based Aerospace and Defense organisation with 121000 employees and revenues of $71.04 billion, Novo Nordisk, a Denmark based Life Sciences organisation with 78554 employees and revenues of $45.92 billion, MediaTek, a Taiwan based Manufacturing organisation with 21982 employees and revenues of $16.17 billion and many others.
Contact us if you need a completed and verified list of companies using NVIDIA DGX Platform, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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BMW | Automotive | 157457 | $166.0B | Germany | NVIDIA | NVIDIA DGX Platform | AI infrastructure | 2019 | n/a |
In 2019, BMW implemented the NVIDIA DGX Platform as core AI infrastructure for its manufacturing operations in Germany. The NVIDIA DGX Platform is used to generate synthetic data and to train deep learning models that target production quality control, factory optimization, and supply-chain simulations.
Deployment centers on high-performance NVIDIA DGX systems and preconfigured DGX software stacks to support model training and production inference workflows. Functional capabilities include synthetic data generation pipelines, supervised and unsupervised model training, and inference orchestration to drive QA automation and factory optimization models. DGX cluster usage is inferred from the case study to provide scalable GPU pooling and parallel training for production-grade workloads.
Operational scope spans R&D through production model training and inference across BMW Group manufacturing operations in Germany, impacting data science teams, quality assurance, production engineering, and supply-chain planning functions. The implementation positions the NVIDIA DGX Platform as the centralized AI infrastructure for manufacturing model lifecycle management from experimentation to production inference.
Governance and rollout integrated DGX based compute into manufacturing model pipelines and operational QA processes, enabling models to move from lab validation to embedded production use. The case study reports up to an 8x boost in data scientist productivity and significant improvements in QA automation as outcomes of the NVIDIA DGX Platform deployment.
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Lockheed Martin | Aerospace and Defense | 121000 | $71.0B | United States | NVIDIA | NVIDIA DGX Platform | AI infrastructure | 2021 | n/a |
In 2021, Lockheed Martin implemented the NVIDIA DGX Platform to build an on-prem AI Factory powered by an NVIDIA DGX SuperPOD. The AI Factory consolidated high-performance GPU compute and MLOps under an AI infrastructure deployment supporting enterprise R&D and engineering in the United States.
The NVIDIA DGX Platform was configured to centralize compute orchestration and MLOps capabilities, supporting large-scale LLM training and hosting. Functional capabilities implemented include training pipelines for generative models, persistent model hosting for internal chatbots and coding assistants, and workflows to manage model lifecycle and experiment tracking.
The deployment operated as an on-prem DGX SuperPOD cluster serving R&D and engineering teams across Lockheed Martin, processing over one billion tokens per week for training and inference workloads. The platform was embedded into developer workflows to shorten iteration cycles and to provide a centralized environment for dataset preparation, model training, and inference serving for internal generative-AI use cases.
Governance changes focused on centralizing compute and standardizing MLOps processes to improve developer productivity and operational control, with rollout concentrated on enterprise R&D and engineering functions. The implementation substantially reduced training time and costs and enabled faster developer productivity and processes, according to the source.
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MediaTek | Manufacturing | 21982 | $16.2B | Taiwan | NVIDIA | NVIDIA DGX Platform | AI infrastructure | 2025 | n/a |
In 2025, MediaTek deployed the NVIDIA DGX Platform as an on premises AI factory powered by an NVIDIA DGX SuperPOD to train and deploy its Breeze series very large language models. This implementation positions the NVIDIA DGX Platform as core AI infrastructure for MediaTek, focused on AI R&D and device and edge model optimization in Taiwan.
The deployment concentrates on high-throughput training and inference pipelines to support very large language model development, model iteration workflows, and device-level optimization. Functional capabilities reported include large-scale model training, inference serving, and accelerated R&D workflows that support frequent model retraining and iteration on the Breeze series.
Operationally the DGX SuperPOD cluster is hosted on premises in Taiwan and is used by MediaTek research and engineering teams responsible for AI R&D and device and edge model tuning. The implementation supports both training and deployment workflows, enabling tighter coordination between model development and device optimization efforts across product engineering and research groups.
MediaTek reports tens of billions of tokens processed monthly and large gains in training and inference throughput, with faster inference and higher token throughput enabling accelerated model iteration. The narrative emphasizes the NVIDIA DGX Platform as AI infrastructure that materially changed throughput and iteration cadence for MediaTeks AI R&D and device model optimization efforts.
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Life Sciences | 78554 | $45.9B | Denmark | NVIDIA | NVIDIA DGX Platform | AI infrastructure | 2025 | n/a |
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