Key Takeaways:
- AI deployments require end-to-end infrastructure solutions that optimize time-to-market and system performance.
- Ethernet is now capable of meeting AI’s low-latency demands, combining speed, flexibility, and scalability.
DDN provides the data intelligence platforms needed to eliminate bottlenecks and accelerate AI deployment at scale.
In a recent interview with Marc Hamilton, NVIDIA’s VP of Solutions Architecture and Engineering, we gained invaluable insights into the successful deployment of AI at scale. With a rich background in performance computing at industry giants like HP, Sun, and TRW, Marc now works closely with NVIDIA customers and partners to deliver best-in-class AI, deep learning, and high-performance computing solutions.
This interview offers a broad overview of the AI landscape, focusing on key trends, use cases, and the necessity for fast, complementary solutions to achieve AI success. As DDN has recently been certified as a tier-one data solution provider within the NVIDIA Partner Network, our collaboration has produced robust reference architectures designed to accelerate AI implementation and outcomes.
AI: A Data Center Scale Problem
Marc aptly summarizes the modern AI challenge: “AI is a data center-scale problem.” AI is no longer about isolated components but rather about delivering end-to-end solutions where every element works seamlessly together. As companies move from training large models to fine-tuning and deploying them, the focus has shifted to time-to-market, a metric that is tightly coupled with system performance.
At DDN, we understand that AI requires more than just powerful GPUs. As NVIDIA’s GPUs, like Blackwell, push the boundaries of training and inference performance with up to 30x improvements, the critical that the data platform keeps pace. DDN platforms ensure that no part of the AI architecture becomes a bottleneck. When organizations invest millions in GPUs, they need a data intelligence platform and networking systems that accelerate—not limit—performance.
The Shift Back to Ethernet for AI Networking
Historically, Ethernet has struggled with the low-latency requirements of AI workloads, leading to the dominance of Infiniband in high-performance networking. However, with NVIDIA’s developments like Spectrum-X and BlueField, Ethernet has been redesigned for AI, offering the speed of Infiniband with the flexibility and compatibility of Ethernet.
The AI community is starting to see the benefits of these advancements, especially when coupled with DDN’s fast, multi-tenant data intelligence platforms that scale effortlessly to meet the demands of modern AI deployments in both on-premises and cloud environments.
DDN’s Role in the AI Ecosystem
Just as AI needs a different kind of network, it also demands a new class of data intelligence platforms. NVIDIA recognized this when they first partnered with DDN over 7 years ago to scale performance for their DGX SuperPODs™.
Since then, DDN has consistently delivered performance and scalability for AI workloads across industries, from virtual AI applications to enterprises manufacturing tangible goods.
Our EXAScaler® solution, for instance, was the first data intelligence platform system certified for the DGX SuperPOD™, offering unparalleled performance and scalability for AI at any size. Today, as AI continues to evolve beyond virtual applications to enterprises that require real-world simulations and inference capabilities, DDN’s data intelligence platform ensures that every phase of AI—from training to inference—is optimized for speed and efficiency.
Cloud and Volume Supercomputing: The Future of AI
AI workloads are increasingly moving to the cloud, and modern AI environments require data intelligence platforms that are as flexible as they are powerful. DDN’s Infinia platform is designed to meet these needs with a hyper-simple, multi-protocol, software-defined solution that excels in cloud and on-prem environments. Infinia’s dynamic multi-tenancy and automated QoS ensure that performance is never compromised, regardless of scale or location.
The collaboration between DDN and NVIDIA has allowed both companies to build reference architectures (aka blueprints) that enable the next generation of AI. DDN’s solutions not only scale to meet the demands of enterprise AI, but they also simplify the management of large-scale AI platforms, making it easier than ever for organizations to adopt AI and realize its full potential.
Expanding into Cloud Environments
The SuperPOD™ reference architecture, initially designed for on-prem AI workloads, is now being deployed by cloud providers at an unprecedented scale. These deployments mark the next phase of the DDN-NVIDIA partnership, where cloud-native, high-performance data intelligence platforms like DDN’s Infinia are changing the game for enterprise AI. By combining Infinia’s scalability with the power of NVIDIA’s AI systems, we are enabling enterprise customers to scale AI workloads across tens of thousands of GPUs while maintaining peak performance.
Watch the full interview with Marc Hamilton here.
