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AI deployments are becoming increasingly complex, with workloads distributed across cloud, edge, and on-premise data center infrastructure. Managing and orchestrating generative AI, recommender systems, search engines, and other workloads requires sophisticated scheduling to optimize performance at the system level and on the underlying infrastructure.
AI and digital twin provider NVIDIA has finalized its acquisition of Run:ai, a provider of Kubernetes-based GPU workload management and orchestration software. First announced in April 2024, the acquisition is intended to enable better GPU utilization, improved management of GPU infrastructure, and enable greater flexibility through open architecture.
Run:ai enables enterprise customers to manage and optimize their compute infrastructure, whether on-premise, in the cloud, or in hybrid environments. While Run:ai’s offerings currently support only NVIDIA GPUs, it supports all popular Kubernetes variants and integrates with third-party AI tools and frameworks. The company announced plans to open source the Run:ai software to extend its availability to the entire AI ecosystem.
The Run:ai platform includes:
A centralized interface to manage shared compute infrastructure.
Functionality to add users, curate them under teams, provide access to cluster resources, control over quotas, priorities and pools, and monitor and report on resource use.
The ability to pool GPUs and share computing power—from fractions of GPUs to multiple GPUs or multiple nodes of GPUs running on different clusters—for separate tasks.
Efficient GPU cluster resource utilization.
Run:ai has been a close collaborator with NVIDIA since 2020, with a customer base that includes some of the world’s largest enterprises across multiple industries that use the Run:ai platform to manage data-center-scale GPU clusters. NVIDIA HGX, DGX, and DGX Cloud customers will gain access to Run:ai’s capabilities for their AI workloads, particularly for large language model deployments. Run:ai’s solutions are already integrated with NVIDIA DGX, NVIDIA DGX SuperPOD, NVIDIA Base Command, NGC containers, and NVIDIA AI Enterprise software, among other products.
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