In a move to bolster AI workload management, NVIDIA has acquired Run:ai, an Israeli startup specializing in efficient cluster resource utilization. This acquisition aims to enhance the management and optimization of compute infrastructure, offering customers improved efficiency and flexibility across various deployment environments.
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Israeli startup promotes efficient cluster resource utilization for AI workloads across shared accelerated computing infrastructure.
NVIDIA’s Acquisition of Run:ai
To help customers make more efficient use of their AI computing resources, NVIDIA today announced it has entered into a definitive agreement to acquire Run:ai, a Kubernetes-based workload management and orchestration software provider.
Complexity of AI Deployments
Customer AI deployments are becoming increasingly complex, with workloads distributed across cloud, edge, and on-premises data center infrastructure.
Sophisticated Workload Management
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.
| Topic | Summary |
|---|---|
| Introduction | NVIDIA’s acquisition of Run:ai aims to enhance AI workload management by leveraging its Kubernetes-based software for efficient cluster resource utilization. |
| Complexity of AI Deployments | AI deployments are increasingly complex, spread across cloud, edge, and on-premises infrastructure, requiring sophisticated workload management for optimal performance. |
| Run:ai’s Solution | Run:ai enables enterprise customers to manage and optimize compute infrastructure across various environments, offering a centralized interface and functionality to control resources and monitor usage. |
| Built on Kubernetes | Run:ai’s open platform is built on Kubernetes, supporting popular variants and integrating with third-party AI tools and frameworks, providing scalability and flexibility for modern AI and cloud infrastructure. |
| Benefits for AI Developers | The platform offers a centralized interface, user management features, and GPU pooling capabilities, ensuring efficient utilization and easier access for AI workloads, ultimately maximizing compute investments. |
| Integration with NVIDIA Products | Run:ai’s capabilities will be integrated with NVIDIA products like HGX, DGX, and DGX Cloud, enhancing AI workload management, particularly for large language model deployments, and supporting a broad ecosystem of third-party solutions. |
| Continued Support and Investment | NVIDIA will maintain Run:ai’s products under the same business model, investing in its roadmap, including integration with NVIDIA DGX Cloud, ensuring continued support and innovation for customers. |
| Enhancing Ecosystem Support | The collaboration between NVIDIA and Run:ai will offer customers a unified fabric for GPU access, supporting a wide range of third-party solutions, providing better GPU utilization and management flexibility. |
| Conclusion | Together, NVIDIA and Run:ai aim to improve GPU utilization, enhance management of GPU infrastructure, and provide greater flexibility in AI workload management, benefiting customers across various industries. |
Run:ai’s Solution
Run:ai enables enterprise customers to manage and optimize their compute infrastructure, whether on premises, in the cloud, or in hybrid environments.
Built on Kubernetes
The company has built an open platform on Kubernetes, the orchestration layer for modern AI and cloud infrastructure. It supports all popular Kubernetes variants and integrates with third-party AI tools and frameworks.
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Customer Base
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.
Benefits for AI Developers
The Run:ai platform provides AI developers and their teams with:
- A centralized interface to manage shared compute infrastructure, enabling easier and faster access for complex AI workloads.
- 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, enabling customers to gain more from their compute investments.
Integration with NVIDIA Products
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.
Continued Support and Investment
NVIDIA will continue to offer Run:ai’s products under the same business model for the immediate future. Additionally, NVIDIA will continue to invest in the Run:ai product roadmap, including enabling on NVIDIA DGX Cloud.
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Enhancing Ecosystem Support
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.
Conclusion
Together with Run:ai, NVIDIA will enable customers to have a single fabric that accesses GPU solutions anywhere. Customers can expect to benefit from better GPU utilization, improved management of GPU infrastructure, and greater flexibility from the open architecture.
FAQs: NVIDIA’s Acquisition of Run:ai
Q: What is the significance of NVIDIA’s acquisition of Run:ai?
A: NVIDIA’s acquisition of Run:ai aims to enhance AI workload management by providing customers with efficient cluster resource utilization across shared accelerated computing infrastructure.
Q: What is Run:ai’s primary focus in AI workload management?
A: Run:ai specializes in Kubernetes-based workload management and orchestration software, catering to the needs of enterprises managing complex AI deployments.
Q: How does Run:ai address the complexity of AI deployments?
A: Run:ai enables enterprises to manage and optimize their compute infrastructure, whether on-premises, in the cloud, or in hybrid environments, thereby simplifying the management of distributed AI workloads.
Q: What are the key features of Run:ai’s platform for AI developers?
A: Run:ai provides AI developers with a centralized interface for managing shared compute infrastructure, functionality to control user access, quotas, and priorities, as well as the ability to efficiently utilize GPU clusters for various tasks.
Q: How does Run:ai integrate with NVIDIA products?
A: Run:ai’s solutions are integrated with NVIDIA HGX, DGX, and DGX Cloud, offering customers access to enhanced AI workload management capabilities, particularly for large language model deployments.
Q: Will Run:ai’s products undergo any changes post-acquisition by NVIDIA?
A: NVIDIA plans to continue offering Run:ai’s products under the same business model for the immediate future and will invest in further enhancing the Run:ai product roadmap.
Q: What benefits can customers expect from the integration of NVIDIA and Run:ai?
A: Customers can anticipate better GPU utilization, improved management of GPU infrastructure, and greater flexibility in accessing GPU solutions across different environments, resulting from the collaboration between NVIDIA and Run:ai.