Scalable AI-Ready Infrastructure

Designing for Real-World Deep Learning Use Cases

Scalable AI Infrastructure: Designing for Real-World Deep Learning Use Cases report, reference architecture in a 1:5 configurationDeep learning (DL) is enabling rapid advances in some of the grandest challenges in science today: in medicine, physics, and autonomous vehicles. All have a common element–data. DL is fundamentally driven by data.

Graphics processing units (GPUs) enable new insights that were not previously possible. To meet the rigorous demands of the GPUs in a DL application, storage systems must able to constantly feed data to the GPUs at low latencies and high throughput, regardless of data type.

As organizations progress from small-scale DL deployments to production, it’s crucial to design an infrastructure that can deliver high performance and allow independent and seamless scaling.

NetApp and NVIDIA solution

NetApp has partnered with NVIDIA to introduce a rack-scale architecture enabling organizations to start small and expand the infrastructure smoothly. Find out how the purpose-built infrastructure can:

  • Allow organizations to deploy deep-learning workloads
  • Handle the unique demands of DL
  • Provide the fastest time to training

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Learn more about NetApp and Nvidia’s partnership

Check out the “Accelerate Your Journey to AI with NetApp and NVIDIA” video and learn how to simplify, accelerate and integrate your data pipeline for deep learning with NetApp ONTAP AI, powered by NVIDIA DGX supercomputers and NetApp cloud-connected, all-flash storage.


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