Deep Learning Training:

Faster, Easier &
Under Control

Run:AI virtualizes and accelerates deep learning. Our automated distributed training technology and neural network analysis allows companies to speed up time-to-delivery and train bigger models, while simplifying compute infrastructure processes and reducing costs.

Take Control of Training Times and Costs

Deep learning requires a completely new approach to compute infrastructure. Workloads are time and compute intensive and each neural network is different. This creates a significant scalability gap that results in longer time-to-delivery and higher costs.

The Run:AI platform solves this through virtualization, by adding acceleration and better controls to rein in unpredictable processes.

10x Speedups and Bigger Models

Run:AI’s unique computational graph analysis technology provides automatic distributed training using a hybrid combination of data and model parallelisms. This breaks the boundaries of GPU memory to enable the training of models of any size.

This means data scientists and deep learning engineers can effortlessly run bigger models several times faster, while reducing costs and maximizing server utilization.

The Run:AI Platform

Our platform manages all the training requests within an organization

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Our software automatically analyzes each workload’s computational complexity and optimizes computations

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Run:AI automatically chooses and executes the distributed training strategy, and optimizes resource allocation

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Focus on your Model, We’ll Do the Rest

Our software analyzes your workload, chooses the optimal hardware parallelism configuration and schedule, and automatically executes it — with full tracking and visibility.

It’s as simple as typing a single command, without requiring code changes by the user.

Set Policies that Make Sense

With many users, models, experiments, and workloads vying for compute resources and budgets, it’s difficult to make informed decisions. Run:AI gives you the ability to set policies and preferences for deep learning training for the many concurrent processes running across your organization, along with information to help you predict and re-arrange schedules and budgets.


Run on any public and private cloud

Automated experiment scheduling

Optimize for speed or cost

GPU infrastructure management

Full control, visibility and prioritization tools

No code changes required by the user

1-click execution of experiments

Optimal GPU configuration per model

Integrated with

Technology Partners


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