The race to scale artificial intelligence is pushing infrastructure to its limits. Across enterprises, sovereign AI initiatives, and cloud providers, organizations are deploying massive NVIDIA GPU clusters to power training, inference, and emerging agentic workloads.
But GPUs alone are not enough.
To unlock the full value of modern AI infrastructure, compute must be paired with high-performance networking and intelligent data platforms that keep GPUs continuously fed with data. When networking and storage cannot keep pace, GPU utilization drops, AI pipelines slow down, and infrastructure investments fail to deliver expected results.
That’s why DDN and Cisco are expanding their collaboration, bringing together Cisco’s next-generation AI networking innovations and the DDN Data Intelligence Platform to help organizations build faster, more efficient AI clusters.
A Growing Cisco and DDN Partnership for AI Infrastructure
Cisco recently introduced the Silicon One G300 platform and new Cisco N9000 and Cisco 8000 networking systems, designed to power massive scale-out AI clusters built around NVIDIA GPU architectures for training, inference, and real-time AI workloads. These systems deliver 102.4 Tbps switching performance, advanced telemetry, and high-density optics to support the demanding requirements of modern AI infrastructure.
Combined with Cisco Nexus One for AI networking, these innovations create a flexible, scalable networking fabric capable of supporting everything from enterprise AI deployments to hyperscale GPU clusters.
At the same time, DDN continues to expand the capabilities of the DDN Data Intelligence Platform, powered by EXAScaler and Infinia, to deliver the high-performance AI storage and data orchestration required for modern AI workloads.
Together, Cisco and DDN are helping customers build a unified AI infrastructure stack where networking, compute, and data systems work as a coordinated platform, maximizing GPU utilization and dramatically improving AI cluster efficiency for NVIDIA-based AI infrastructure deployments.
Why AI Clusters Require Both Intelligent Networking and AI Data Platforms
Modern AI workloads generate enormous data flows between GPUs, storage systems, and distributed training nodes. Even small delays or packet loss can stall training jobs and reduce overall infrastructure efficiency.
Cisco’s AI networking architecture addresses these challenges with high-performance Ethernet fabrics, advanced congestion control, and unified management through Cisco Nexus One. This allows AI infrastructure teams to deploy scalable, resilient networking fabrics that support RDMA and lossless communication across GPU clusters.
Meanwhile, the DDN Data Intelligence Platform ensures that AI storage and data pipelines operate at the speed required by modern GPUs.
With multi-terabyte-per-second throughput, millions of IOPS, and sub-millisecond latency, DDN’s AI storage architecture eliminates data bottlenecks that often cause GPUs to sit idle. The result is significantly higher GPU utilization and dramatically faster training cycles.
When combined, Cisco’s networking innovation and DDN’s AI data platform provide the infrastructure foundation required for efficient, large-scale AI clusters.
Infinia: Scaling the Next Generation of AI Data Platforms
As AI infrastructure expands to thousands—and even hundreds of thousands—of GPUs, organizations need data platforms that can manage and orchestrate massive volumes of AI data across distributed environments.
DDN Infinia is designed to meet this challenge.
As part of the DDN Data Intelligence Platform, Infinia provides a modern architecture for AI data pipelines, supporting the full lifecycle of AI workloads including data ingestion, preparation, inference, and retrieval-augmented generation.
Infinia enables organizations to:
- Deliver scalable AI data pipelines for training, inference, and data preparation
- Support large-scale AI environments with secure multi-tenant data access
- Accelerate AI data pipelines with high-performance metadata and object services
- Provide flexible data services for AI applications and AI-as-a-service environments
Combined with Cisco Nexus One networking, Infinia helps ensure that AI data moves efficiently across distributed infrastructure, connecting data sources, compute environments, and AI services.
The result is more efficient AI infrastructure, improved GPU utilization, and faster time to insight for AI-driven applications.
Maximizing AI Cluster Efficiency and GPU Utilization
One of the biggest challenges facing AI infrastructure teams today is ensuring that GPU clusters operate efficiently at scale.
Even small inefficiencies in networking or storage performance can cause GPU stalls that dramatically increase training times and infrastructure costs.
The joint Cisco and DDN architecture directly addresses these challenges by combining:
High-performance AI networking
Cisco Nexus networking and Silicon One technology deliver low-latency, lossless connectivity optimized for GPU cluster communication.
AI-optimized storage and data pipelines
The DDN Data Intelligence Platform delivers the performance and scalability required to keep GPUs fully utilized.
Integrated observability and operations
Cisco Nexus Dashboard and telemetry provide visibility across networking and AI workloads, enabling teams to detect bottlenecks and optimize infrastructure performance.
Together, these capabilities allow organizations to dramatically improve AI cluster efficiency, reduce job completion times, and maximize the return on GPU investments.
Building the AI Infrastructure for the Next Wave of Innovation
The next generation of AI breakthroughs will depend on infrastructure that can scale as quickly as innovation itself.
By combining Cisco’s leadership in AI networking with the DDN Data Intelligence Platform and Infinia, organizations can build AI infrastructure that supports everything from enterprise AI deployments to hyperscale AI factories.
For AI infrastructure architects, platform teams, and cloud providers, the message is clear:
The future of AI depends not just on GPUs, but on the intelligent infrastructure that powers them.
Learn More
To learn how Cisco and DDN are advancing AI infrastructure for GPU clusters, explore:
- Cisco Nexus One AI networking solutions
- The DDN Data Intelligence Platform
- DDN Infinia for next-generation AI data platforms
Or connect with the DDN team at NVIDIA GTC to see how these technologies are enabling the next generation of AI infrastructure.
AI infrastructure refers to the combination of GPUs, networking, storage, and software platforms required to train and deploy AI models at scale. High-performance networking and AI-optimized storage are essential for maintaining GPU utilization and efficient AI pipelines.
GPU utilization directly impacts the efficiency and cost of AI workloads. If GPUs are waiting on data due to storage or networking bottlenecks, training times increase and infrastructure investments become less efficient.
AI storage provides the high-throughput data pipelines required to feed GPU clusters during model training and inference. Modern AI clusters require AI storage platforms capable of delivering massive parallel data throughput with minimal latency.
The DDN Data Intelligence Platform is a high-performance AI data platform designed to power AI infrastructure at scale. It provides advanced AI storage, data orchestration, and multi-tenant capabilities that enable efficient AI clusters and large-scale AI deployments.
Cisco provides high-performance AI networking through technologies like Silicon One and Nexus One, while DDN delivers AI storage and data platform capabilities through the Data Intelligence Platform and Infinia. Together they enable scalable, efficient AI infrastructure.