11.09.20BACK TO MENU
SCAN ANNOUNCES PARTNERSHIP WITH RUN:AI
Scan is proud to announce its new partnership with Run:AI, a company virtualising AI infrastructure, to ensure maximum resource utilisation to its portfolio of GPU-accelerated AI and deep learning workstations and servers.
Data science workloads often need prolonged but often inconsistent access to multiple computing resources such as GPUs. Typically, data scientists are statically allocated a few GPUs each, with those expensive hardware resources sitting idle when not used. IT departments struggle to allocate the right amount of resources to data science teams, suffering from poor visibility and a lack of control, leading to data scientists either having more GPU capacity than they can currently use, or being limited when they try to run large experiments.
Instead of statically assigning GPUs to data scientists, Run:AI creates a pool of GPU resources, and will automatically and elastically ‘stretch’ a workload to run over multiple GPUs if they’re available. Important jobs can be given guaranteed quotas which they can also exceed, and Run:AI’s software will elastically and automatically scale the workloads to the available hardware based on defined priorities. This technology offers the perfect fit when paired with Scan 3XS Deep Learning Workstations and NVIDIA Data Science Workstations for development workloads, or when used with NVIDIA DGX servers during training of AI models, ensuring maximum uptime and productivity from any hardware investment.
“The nature of Run:AI being available on all our GPU-accelerated hardware offerings benefit any size of organisation. The software can be installed on existing GPU systems, and be configured on new systems from Scan in order that any growing GPU pool is utilised to the greatest degree. No matter where you are on the AI journey, Run:AI is an essential tool to make hardware assets work as hard as possible’ said Elan Raja, CEO of Scan Computers. He further added “Our aim is also to provide pre-built Run:AI appliances - servers designed and optimised to deliver guaranteed benchmarked GPU performance to a pre-defined number of students - something we’ve long been asked for from the higher educational sector”.
“We are excited to partner with Scan Computers, a leading UK IT solutions provider, to help their customers speed delivery of AI solutions to market,” said Omri Geller, co-founder and CEO of Run:AI. “We offer a flexible system that can pool resources between users and allocate compute more effectively, creating maximum ROI for a company’s resources and accelerating innovation."
To learn more about adding Run:AI software to your systems, or understand more about Run:AI ready workstations and servers, contact the Scan AI team on [email protected], or visit https://www.scan.co.uk/business/deep-learning
At Scan we are all about three things – technology, people, and how we bring them together and for over 30 years we’ve been driven by these three passions. We work with customers and partners to tackle the latest cutting-edge areas in IT and bridge the gap these challenges present.
Building on our reputation within the computing industry, we have used this knowledge and experience to provide specialist IT solutions for business verticals including deep learning and AI, healthcare, financial services, retail, education, AEC and manufacturing.
Run:AI has built the world's first orchestration and virtualization platform for AI infrastructure. By abstracting workloads from underlying hardware, Run:AI creates a shared pool of resources that can be dynamically provisioned, enabling efficient orchestration of AI workloads and optimized utilization of expensive GPUs. Data Scientists can seamlessly consume massive amounts of GPU power to improve and accelerate their research while IT teams retain centralized, cross-site control and real-time visibility over resource provisioning, queuing, and utilization - whether on premises or in the cloud. The Run:AI platform is built on top of Kubernetes, enabling simple integration with existing IT and data science workflows