Run:ai’s cloud-native compute orchestration platform, Atlas, helps enterprises dramatically reduce the time to train and productize AI models by creating a virtual pool of compute resources and automating allocation. With dynamic, workload-aware scheduling, IT can achieve dreamed-about levels of GPU utilization, and ensure business goals are met with custom prioritization rules and dashboards. Data scientists can start experiments and run hundreds of training jobs without ever touching code. Run:ai partnered with Microsoft and NetApp to address a lane-detection use case by building a distributed training deep learning solution at scale that runs in the cloud. This solution enables data scientists to fully embrace cloud scaling capabilities and cost benefits for automotive use cases. Since its inception in 2018, Run:ai has continued to break through the known limits of GPU technology, releasing multiple new capabilities in rapid succession, such as fractional GPU allocation, thin GPU provisioning, job swapping, and dynamic scheduling for NVIDIA’s Multi-Instance GPU (MIG) technology. It is the only AI infrastructure solution boasting near-100% GPU utilization for its enterprise customers. As cited in The Forrester Wave: AI Infrastructure, Q4 2021, Run:ai offers enterprises “complete flexibility in the hardware they choose to use and where they choose to run it.”

https://www.run.ai/