Los Angeles — DDN today announced deep collaboration with NVIDIA to support the company’s next-generation AI factory architecture unveiled at CES 2026, including the NVIDIA Rubin platform and NVIDIA BlueField-4 DPU.
DDN and NVIDIA are enabling enterprises and hyperscalers to operationalize large-scale AI faster by eliminating data bottlenecks that limit performance, utilization, and time-to-value.
As generative and agentic AI workloads evolve toward million-token context windows, distributed inference, and data-intensive reasoning, organizations are discovering that raw compute alone is no longer enough. Business outcomes now depend on how efficiently data is moved, cached, secured, and served across GPUs, DPUs, networks, and storage.
That is where DDN and NVIDIA’s approach delivers measurable impact.
“AI factories succeed or fail based on data efficiency,” said Alex Bouzari, CEO and co-founder at DDN. “Our collaboration with NVIDIA is focused on a single outcome: ensuring that the world’s most advanced AI platforms—powered by NVIDIA Rubin and BlueField-4—are fed with data at full speed, at full scale, and with predictable performance. When data intelligence is built in, customers see higher GPU utilization, faster inference, and dramatically lower operational friction.”
The NVIDIA Rubin platform represents a fundamental shift from accelerator-centric design to rack-scale AI factory architectures using co-design to tightly integrate CPUs, GPUs, DPUs, NVLink fabrics, and high-speed Spectrum-X Ethernet networking.
DDN works with NVIDIA to ensure its AI data intelligence platform is engineered to operate natively within this unified stack — translating architectural innovation into real-world results, including:
- Up to 99% GPU utilization across large-scale AI environments
- 20–40% reduction in time-to-first-token (TTFT) for long-context inference workloads
- Faster time-to-model deployment through simplified, integrated data pipelines
- Lower infrastructure overhead by reducing CPU load and eliminating inefficient data movement
DDN’s platform is engineered to align with NVIDIA’s latest hardware and software innovations, including NVIDIA Spectrum-X Ethernet for Storage and NVIDIA DOCA-accelerated services on BlueField-4, enabling:
- Exascale data access to feed high-density Rubin GPU configurations at line rate
- Distributed KV cache tiering supporting the NVIDIA Inference Context Memory Storage Platform, which expands inference context beyond GPU memory while maintaining ultra-low latency
- Network-integrated storage services that leverage BlueField-4 acceleration engines for metadata processing, telemetry, and control-plane operations
- Dynamic, telemetry-driven data placement to optimize performance as workloads shift in real time
In addition to being a certified storage solution for NVIDIA DGX SuperPOD, DDN already powers more than 1,000,000 GPUs across the world’s most demanding AI and HPC environments, ensuring consistent performance as models scale in size, complexity, and concurrency.
As AI moves from experimentation to production, enterprises must balance performance with governance, security, and operational efficiency. Together, DDN and NVIDIA enable organizations to run AI pipelines that are not only faster but also more secure and manageable.
With integrated data intelligence and BlueField-4 offload capabilities, customers can:
- Secure AI data end-to-end, at rest and in motion
- Enforce multi-tenant isolation across shared AI infrastructure
- Gain real-time visibility into data access patterns and performance bottlenecks
- Reduce audit and compliance preparation time by up to 70% through unified observability and access intelligence
The future of AI infrastructure is unified—across compute, networking, and data. NVIDIA Rubin and BlueField-4 platforms exemplify this shift, and DDN’s AI data intelligence platform ensures that data becomes a competitive advantage rather than a constraint.
By collaborating with NVIDIA, DDN is helping customers transform AI infrastructure into AI factories — systems designed not just to compute, but to deliver outcomes faster, at scale, and with confidence.
To learn more, visit ddn.com/nvidia.
