List of NVIDIA Run:ai Customers
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Since 2010, our global team of researchers has been studying NVIDIA Run:ai 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 Run:ai for ML and Data Science Platforms 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 Run:ai for ML and Data Science Platforms include: University of Pennsylvania, a United States based Education organisation with 39859 employees and revenues of $14.40 billion, King's College London, a United Kingdom based Education organisation with 8500 employees and revenues of $1.55 billion, Playstation Ireland, a Ireland based Media organisation with 100 employees and revenues of $500.0 million and many others.
Contact us if you need a completed and verified list of companies using NVIDIA Run:ai, 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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King's College London | Education | 8500 | $1.6B | United Kingdom | NVIDIA | NVIDIA Run:ai | ML and Data Science Platforms | 2020 | n/a |
In 2020, King's College London installed NVIDIA Run:ai in its London Medical Imaging & AI Centre to virtualize and orchestrate GPU resources for medical imaging and healthcare research. The deployment used NVIDIA Run:ai within the ML and Data Science Platforms category to centralize GPU capacity and enable concurrent experimentation across clinical AI projects.
NVIDIA Run:ai was configured to provide GPU resource pooling and orchestration, enabling workload scheduling and automated allocation of accelerator capacity across research teams. The implementation emphasized virtualization of GPU resources, allowing researchers to submit and run experiments against a shared GPU pool rather than isolated machines, which optimized utilization and scheduling of training workloads.
Operational scope covered the London Medical Imaging & AI Centre's healthcare research programs and clinical AI project pipelines, with researchers leveraging the pooled resources to accelerate model training and inference experimentation. The centre reported a doubling of GPU utilization and execution of over 300 experiments in a 40 day period, outcomes that directly accelerated experimentation and reduced time to results for clinical AI projects.
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Playstation Ireland | Media | 100 | $500M | Ireland | NVIDIA | NVIDIA Run:ai | ML and Data Science Platforms | 2022 | n/a |
In 2022, Playstation Ireland adopted NVIDIA Run:ai within its ML and Data Science Platforms to support ML platform engineering and MLOps. Playstation Ireland job postings in Dublin list NVIDIA Run:ai experience as a preferred qualification, indicating NVIDIA Run:ai is used to orchestrate GPU accelerated ML workloads within the ML platform and engineering teams.
The hiring signal implies operational use of GPU orchestration and MLOps functionality typical of ML and Data Science Platforms, including workload scheduling, multi tenant GPU allocation and cluster level orchestration for training jobs and experiment management. Deployment and operational scope are concentrated on PlayStation's Dublin ML platform and engineering teams, where governance is reflected in role based hiring requirements for platform engineers responsible for workload scheduling and resource governance.
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University of Pennsylvania | Education | 39859 | $14.4B | United States | NVIDIA | NVIDIA Run:ai | ML and Data Science Platforms | 2023 | n/a |
In 2023, the University of Pennsylvania School of Engineering & Applied Science deployed NVIDIA Run:ai as the combined resource manager and scheduler for its AI clusters in the United States. NVIDIA Run:ai is employed as part of the ML and Data Science Platforms footprint to provide centralized orchestration for GPU research workloads across Penn SEAS infrastructure.
The implementation runs either SLURM or a Kubernetes orchestrator over a container platform that is managed via NVIDIA Run:ai, delivering consistent, repeatable runtime environments for containerized training and inference. Functional capabilities emphasized in documentation include cluster-level resource management, job scheduling, and GPU allocation, enabling orderly queuing and execution of large-scale research jobs.
Operational coverage is focused on Penn SEAS AI clusters supporting engineering and applied science research, where the configuration simplifies scheduling and resource allocation as described in Penn SEAS documentation. The deployment centralizes orchestration across heterogeneous orchestrators, aligning compute provisioning, container lifecycle management, and resource governance for faculty and research groups.
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