Run:ai Atlas Platform

Access 100% of your compute power, no matter when you need it, to accelerate AI development


Getting the most out of your AI infrastructure

It is estimated that more than 80% of all AI models don't make it to production. One significant reason for this is that AI requires an entirely new infrastructure stack - including frameworks, software and hardware accelerators. These valuable resources are complex to manage and are often left sitting idle due to static allocation. When resources are allocated to teams or researchers who aren’t actively using them, it wastes compute that could otherwise be used for other tasks. The Run:ai Atlas platform breaks this paradigm by creating virtual pools of GPUs and automatically allocating the right amount of compute for every task, from huge distributed computing workloads to small inference jobs.


Run:ai Atlas automates resource management and consumption so that users can easily access GPU fractions, multiple GPUs or clusters of GPUs for workloads of every size and stage of the AI lifecycle. This ensures that all available compute can be utilised and GPUs never have to sit idle. Whenever extra compute resources are available, data scientists can exceed their assigned quota, speeding time to results and ultimately meeting the business goals of their AI initiatives.

Pool GPU Compute

Centralise AI

Pool GPU compute resources so IT gains visibility and control over resource prioritisation and allocation

Guaranteed Quotas

Maximise Utilisation

Automatic and dynamic provisioning of GPUs breaks the limits of static allocation to get the most out of existing resources


Deploy to Production

An end-to-end solution for the entire AI lifecycle, from developing to training and inferencing, all delivered in a single platform

How Run:ai Atlas Works

Run:ai’s Atlas scheduler is a simple plug-in for Kubernetes clusters and adds high-performance orchestration to your containerised AI workloads. Using multi- and hybrid-cloud platforms, powered by a cloud-native operating system, Atlas supports running AI initiatives anywhere - on-premises, on the edge or in the cloud. The Run:ai dynamic workload-aware scheduler requires no advanced setup, and can work with any number of Kubernetes orchestration flavours such as vanilla, RedHat OpenShift and HPE Container Platform. The GPU abstraction layer also offers deep integration into AI accelerators and enables efficient sharing and automated configuration of these resources across multiple workloads.


For data scientists who prefer not to interact directly with code, Run:ai offers a native ResearcherUI and support for a wide variety of popular ML tools as well as command line. Researchers can launch jobs with their choice of tools, and Run:ai’s scheduler automatically and fairly allocates resources. There’s no need for IT to manually provision GPUs. IT simply sets custom rules and prioritisation based on their organisation’s goals

Single Pane of Glass

Gain centralised & multi-tenant management of resources, utilisation, health and performance across any aspect of the AI pipeline, no matter where the workloads are run. Run:ai’s dashboard offers real time and historical views of all resources managed by the platform, including jobs, deployments, projects, users, GPUs and clusters.

Simple Workload Scheduling

The Run:ai scheduler manages tasks in batches using multiple queues on top of Kubernetes, allowing system admins to define different rules, policies and requirements for each queue based on business priorities. Bridging the efficiency of high-performance computing and the simplicity of Kubernetes, the scheduler allows users to easily make use of fractional GPUs, integer GPUs, and multiple nodes of GPUs for distributed training. In this way, AI workloads run based on needs, not capacity.

Simple Workload Scheduling

The Run:ai Scheduler is a Kubernetes-based software solution for high-performance orchestration of containerised AI workloads. Bridging the efficiency of High-Performance Computing and the simplicity of Kubernetes – the scheduler allows users to easily make use of fractional GPUs, integer GPUs, and multiple-nodes of GPUs, for distributed training. In this way, AI workloads run based on needs, not capacity. Run:ai requires no advanced setup, and can work with any number of Kubernetes orchestration versions including Vanilla, RedHat OpenShift and HPE Container Platform.

Batch Scheduling

This refers to the grouping or batching together of many processing jobs that can run to completion in parallel without user intervention. This way programs run to completion and then free up resources upon completion, making the system much more efficient. Training models can be queued and then launched when resources become available. Workloads can also be stopped and restarted later if resources need to be reclaimed and allocated to more urgent jobs or to under-served users.

Gang Scheduling

Often when using distributed training to run compute intensive jobs on multiple GPU machines, all of the GPUs need to be synchronised to communicate and share information. Gang scheduling is used when containers need to be launched together, start together, recover from failures together, and end together. Networking and communication can be automated between machines by the cluster orchestrator.

Topology Awareness

This concept describes how a researcher can run a container once and get excellent performance and then the next time get poor performance on the same server. The problem comes from the topology of GPU, CPU, and the links between them. The same problem can occur for distributed workloads due to the topology of NICs and the links between GPU servers. The Run:ai scheduler ensures that the physical properties of AI infrastructure are taken into account when running AI workloads, for ideal and consistent performance.

How-to Videos


Optimizing GPU Utilization with Run:ai

Learn about the difference between GPU utilization and GPU allocation and how Run:ai can be used to increase utilization.


Open Demo: Autoscaling Inference on AWS

Join Guy Salton, Director of Solution Engineering from Run:ai, as he discusses Auto-Scaling Inference on AWS.


Training (Batch) Jobs

Get a glimpse of Run:ai Batch (Training) capability as presented by our Solution Engineering Team.

King’s College London

The London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare based at King’s College London (KCL), is using the latest virtualisation software from Run:ai to speed research projects. The software optimises and enhances existing computing resources - NVIDIA DGX-1 and DGX-2 supercomputers plus their associated infrastructure installed and configured by the Scan AI team.

These NVIDIA DGX platforms are used to train algorithms to create AI powered tools for faster diagnosis, personalised therapies and effective screening, using an enormous trove of de-identified public health data from the NHS. The training requires vast amounts of GPU-accelerated compute power, which the DGX appliances provide, but to improve resource allocation and scheduling Run:ai software was added to the KCL platform - this has since doubled its GPU utilisation to support more than 300 experiments within a 40 day period.

Read case study

Guided Proof of Concepts with Scan AI and Run:ai

The Scan AI team is unique in its ability to offer a Proof of Concept (PoC) trial of the Run:ai software platform running on multiple NVIDIA GPUs. This allows you to understand how the scheduling and pooling software will improve your GPU utilisation and there are two options as to how the POC can be conducted:

POC in a customer's on premises environment

In this scenario, a prospect's data scientists can run an evaluation of Run:ai in their own environment, using their own workflows. They can choose one, multiple, or all of their servers to ruin their POC. A trial done this way allows researchers to continue to run experiments with Run:ai in their production environment without having to transfer experiments to a test environment. The on-premises POC allows them to compare Run:ai to their existing tools, easily enabling users to see benchmarks and measure efficiency of the Run:ai system. Currently, prospective customers do not need to purchase a POC license, but this may change in 2021.

POC in a Scan Lab Environment

In the second scenario, a prospective customer can avoid setting up a dedicated production cluster for a POC and instead use a pre-prepared Scan environment to evaluate Run:ai. In this scenario, prospects can try out all of the features of Run:ai, even though they would not be able to see their own data center - for example, pooling disparate resources and scaling distributed training across many nodes, which they can run in the Scan environment even if their own usage of DL / ML is not yet at a large scale. The Scan environment is ready-to-use and already has Kubernetes and Run:ai installed, so customers can avoid the potential inconvenience of installation.

Register for POC

Ways to Purchase

Applying Run:ai software to an existing cluster of GPUs, no matter how disparate, you will see an immediate improvement in how your newly virtualised pool of GPU resource can be scheduled and shared out.

Run:ai software is licensed per GPU you want to virtualise - regardless of the age or specification of any GPU, making for a very easy way to improve productivity and to keep increasing your virtual GPU pool as you add GPUs to your infrastructure.

When choosing new hardware for your AI projects, including the added flexibility that Run:ai software provides couldn’t be easier. For each system simply match the number of Run:ai licences to the number of GPUs in either a 1, 3 or 5 year subscription. Our 3XS build team will install the software, so you have scheduling control and the ability to maximise your GPU utilisation out of the box. Furthermore, if you select Run:ai licences to your system builds every time, your GPU pool will continue to grow seamlessly with each hardware addition - the software will simply ‘discover’ the new GPUs and add them to your resource pool.

The 3XS Systems team and Run:ai has developed a range of certified appliances - designed, tested and configured to get the most out of GPU virtualisation whilst remaining cost effective. They each include a 1-year licence for Run:ai software and cover a range of specifications - from development workstations to server platforms.

Model Development Workstation Training Server Training Server
TF32 Performance 116TF 450TF 656TF
FP64 Performance 1.9TF 7.2TF 41.6TF
Cost £18,999 ex VAT £50,499 ex VAT £62,499 ex VAT
Where to buy View model View model View model
GPUs 4x watercooled NVIDIA
GeForce RTX 3090
GPU Specifications 10,496 CUDA
cores per GPU
10,752 CUDA
cores per GPU
3,804 CUDA
cores per GPU
GPU Memory 24GB GDDR6X per GPU,
96GB total
48GB GDDR6 per GPU,
288GB total
24GB HBM per GPU,
192GB total
GPU Interconnects GPUs paired with NVLink GPUs paired with NVLink GPUs paired with NVLink
CPU AMD Threadripper PRO
3975WX, 32C/64T
2x AMD EPYC 7513,
combined 64C/128T
2x AMD EPYC 7702,
combined 128C/256T
System Memory 256GB ECC Reg DDR4 512GB ECC Reg DDR4 1,024GB ECC Reg DDR4
System Drives 1TB SSD SSD 1TB SSD 2x 1TB SSD
Storage Drives 4TB HDD 4x 3.84TB SSD 4x 3.84TB SSD
Networking 2x 10GbE 2x 200GbE/IB 2x 200GbE/IB
Operating System Ubuntu Linux Ubuntu Linux Ubuntu Linux
Run:ai License 1 Year Subscription 1 Year Subscription 1 Year Subscription
Power Requirement 2,750W 6,600W 6,600W
Dimensions 307 x 697 x 693mm Tower 4U 19in Rackmount 4U 19in Rackmount