Helps Organizations Address Enterprise GPU Consumption Requirements and Ensure Efficient Scaling for AI/ML Workloads
Boston, MA – (June 10, 2024) – Fairwinds , the leading provider of Managed Kubernetes-as-a-Service, today announced that it fully supports artificial intelligence (AI) and machine learning (ML) workloads in Kubernetes by providing GPU-enabled nodes. As demand for AI/ML workloads grows, organizations are increasingly relying on Kubernetes to ensure scalability, flexibility, and optimal resource utilization for these resource-intensive workloads.
GPUs, or graphics processing units, are processors designed to accelerate graphics rendering, capable of processing many pieces of data simultaneously. This makes them ideal for AI, ML, video editing, gaming applications, and more. As organizations increasingly find ways to adopt AI/ML and incorporate these capabilities into their applications and services, they need to ensure efficient resource utilization and the ability to scale seamlessly based on demand.
“To run many AI/ML workloads, you need GPUs or other specialized hardware. However, GPU access in the cloud is more complex than simply spinning up a new node type,” said Andy Suderman, Chief Technology Officer at Fairwinds. “Kubernetes offers scalability and dynamic resource allocation for AI/ML workloads running on GPU worker nodes. On top of that, Fairwinds manages drivers for GPU vendor support, as well as autoscaling, which makes it trivial for our users to utilize GPU resources for use by AI/ML workloads.”
The AI/ML landscape has changed rapidly over the past year and a half, from a focus for small, specialist teams to penetrating every aspect of application development and delivery for organizations of all sizes. For software development and data scientist teams seeking to deploy AI/ML workloads, Kubernetes offers a scalable, efficient infrastructure. This is vital because AI/ML workloads don’t need to run all the time — typically, they experience user and traffic spikes with lulls in between. Fairwinds Managed Kubernetes-as-a-Service can help you ensure that you have the compute resources necessary for AI/ML instantly consumable without worrying about how to build or manage the necessary infrastructure.
“We are already supporting clients on GPU-enabled workloads, ensuring that they can meet customer demand for new, AI-enabled solutions, without unnecessary increases in compute costs,” added Suderman.
