<p>NVIDIA has agreed to acquire Israeli company Run:ai, a Kubernetes-based workload management and orchestration software provider. Terms of the deal weren&#8217;t released.</p>
<p>Customer AI deployments are becoming increasingly complex, with workloads distributed across cloud, edge and on-premises data center infrastructure.</p>
<p>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.</p>
<p>Run:ai enables enterprise customers to manage and optimize their compute infrastructure, whether on premises, in the cloud or in hybrid environments.</p>
<p>The company has built an open platform on <a href="https://www.nvidia.com/en-us/glossary/kubernetes/">Kubernetes</a>, the orchestration layer for modern AI and cloud infrastructure. It supports all popular Kubernetes variants and integrates with third-party AI tools and frameworks.</p>
<p>Run:ai customers include some of the world’s largest enterprises across multiple industries, which use the Run:ai platform to manage data-center-scale GPU clusters.</p>
<p>“Run:ai has been a close collaborator with NVIDIA since 2020 and we share a passion for helping our customers make the most of their infrastructure,” said Omri Geller, Run:ai cofounder and CEO. “We’re thrilled to join NVIDIA and look forward to continuing our journey together.”</p>
<p>The Run:ai platform provides AI developers and their teams:</p>
<ul>
<li aria-level="1">A centralized interface to manage shared compute infrastructure, enabling easier and faster access for complex AI workloads.</li>
<li aria-level="1">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.</li>
<li aria-level="1">The ability to pool GPUs and share computing power — from <a href="https://www.nvidia.com/en-us/technologies/multi-instance-gpu/#:~:text=Multi%2DInstance%20GPU%20(MIG)%20expands%20the%20performance%20and%20value,%2C%20cache%2C%20and%20compute%20cores.">fractions of GPUs</a> to multiple GPUs or multiple nodes of GPUs running on different clusters — for separate tasks.</li>
<li aria-level="1">Efficient GPU cluster resource utilization, enabling customers to gain more from their compute investments.</li>
</ul>
<p>NVIDIA will continue to offer Run:ai’s products under the same business model for the immediate future. And NVIDIA will continue to invest in the Run:ai product roadmap, including enabling on <a href="http://www.nvidia.com/dgx-cloud">NVIDIA DGX Cloud</a>, an AI platform co-engineered with leading clouds for enterprise developers, offering an integrated, full-stack service optimized for generative AI.</p>
<p>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 <a href="https://www.nvidia.com/en-us/data-center/dgx-platform/">NVIDIA DGX</a>, <a href="https://www.nvidia.com/en-us/data-center/dgx-superpod/">NVIDIA DGX SuperPOD</a>, <a href="https://www.nvidia.com/en-us/data-center/base-command/">NVIDIA Base Command</a>, <a href="https://www.nvidia.com/en-us/gpu-cloud/">NGC</a> containers, and <a href="https://www.nvidia.com/en-us/data-center/products/ai-enterprise/">NVIDIA AI Enterprise</a> software, among other products.</p>
<p>NVIDIA’s accelerated computing platform and Run:ai’s platform will continue to support a broad ecosystem of third-party solutions, giving customers choice and flexibility.</p>

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