AI development is based on running a large number of highly compute-intensive training models in parallel, requiring specialized and expensive processors such as GPUs. IT leaders, MLOps, and data science teams find themselves with limited ability to allocate and control expensive compute resources to achieve optimal speed and utilization.

To solve these challenges Run:AI has built the world’s first virtualization layer for deep learning training models. By abstracting workloads from underlying infrastructure, Run:AI creates a shared pool of resources that can be dynamically provisioned, enabling full utilization of expensive GPU compute.


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