Build Your Next
Large Model

Easily train and deploy your AI models, and gain scalable, optimized access to your organization's AI compute. Anywhere.

Screenshot of Run:ai product dashboard
Schema of Run:ai service

Bridging the gap between ML teams and AI infrastructure

Releasing large models is complex and requires powerful GPU resources.

Run:ai abstracts infrastructure complexities and simplifies access to AI compute with a unified platform to train and deploy models across clouds and on premises.

One Platform for all stages of your AI lifecycle

Our platform integrates with your preferred tools and frameworks, leveraging unique scheduling and GPU optimization technologies to simplify and optimize your ML journey, from building to training, and deploying models.

Data Preprocessing

Scale your data processing pipelines to hundreds of machines using one command, with built-in integration with frameworks like Spark, Ray, Dask, and Rapids.

Run your pre-processing pipelines in the same environment with your training workflows, for better efficiency and easier management.

Model Building

Spin up your dev environment in one command and connect it remotely to your favorite IDE and experiment tracking tool in a single click, including built-in support for Jupyter Notebooks, Pycharm, VScode, W&B,, and more.

Access multiple GPUs or a fraction of a single GPU while keeping your data and code private and secure.

Model Training & Fine-tuning

Launch hundreds of distributed batch jobs on shared pools of GPUs without worrying about queueing, infrastructure failures, or GPU provisioning. 

Scale effortlessly from one machine to hundreds of distributed machines, including built-in integration with distributed training frameworks like Pytorch Lightning, Ray, Horovod, and more.

Model Serving (inference)

Keep your compute resources optimized to your model size and SLA to cut inference cost by up to 90%. 

Deploy your models anywhere, from cloud to on-premises and edge servers, and give users secure access to models through URL or a web UI like Gradio or Streamlit.

Accelerate AI development and time-to-market

Iterate fast by provisioning preconfigured workspaces in a click through graphical user interface and scale up your ML workloads with a single command line
Get to production quicker with automatic model deployment on natively-integrated inference servers like NVIDIA Triton

Multiply the Return on your AI Investment

Boost the utilization of your GPU infrastructure with GPU fractioning, GPU oversubscription, consolidation and bin-packing scheduling
Increase GPU availability with GPU pooling, dynamic resource sharing and job scheduling

Take your AI Clusters to the major league

Avoid resource contention with dynamic, hierarchical quotas, automatic GPU provisioning, and fair-share scheduling
Control and monitor infrastructure across clouds and on premises, and protect your organization’s assets with features like policy enforcements, access control, IAM, and more

Supported by the Global AI Computing Leader

Run:ai partnered with NVIDIA to help oganizations achieve the best AI experience. With built-in integration for NVIDIA AI Enterprise software and our close business alliance, Run:ai is the best software to run and manage your NVIDIA GPUs

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NVIDIA Preferred Partner

Trusted by the Best.
Secure by Design.

Run:ai is a state-of-the-art, secure and compliant platform, trusted by industry leaders

Our strong technological and business alliance with NVIDIA, extensive portfolio of commercial clients, and leading research institutions utilizing Run:ai, as well as our global market recognition, enables us to empower the people driving AI innovation

“Our experiments can take days or minutes, using a trickly of computing power or a whole cluster. With Run:ai we’ve seen great improvements in speed of experimentation and GPU hardware utilization”
King's College London
Dr. M Gorge Cardoso
CTO of the AI Centre
Kings College London
“Rapid AI development is what this is all about for us. What Run:ai helps us do is to move from a company doing pure research, to a company with results in production”
Siddharth Sharma
Sr. Research Engineer
“With Run:ai, we take full advantage of our on-prem cluster, and scale to the cloud we need to. Run:ai helps us do that out of the box.”
Andrea Mirabelle
Sr. Manager, Computer Vision
Zebra Technologies
“Run:ai enables us to harness the power of Deep Learning, and continue to innovate through the use of next-generation computational tools for uncovering insights hidden in biological data.”
Talmo Pereira
Fellow & Principal Investigator
Salk Institute

The 2023 State of AI infrastructure

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