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Podcast: Building AI Datacenters with NVIDIA DGX Reference Architectures

Carlo Ruiz is head of EMEA AI Datacenter Solutions at NVIDIA.

In this podcast, Lee Carter from Bright Computing and Carlo Ruiz from NVIDIA discuss building datacenters with NVIDIA DGX Reference Architectures.

NVIDIA DGX servers managed by Bright Cluster Manager deliver the perfect balance of increased concurrency for handling data science workloads, the massive compute requirements associated with those workloads, and the seamless management of all resources across the HPC cluster. Join this podcast to learn more about the power behind this partnership.

Bright and NVIDIA have also composed a detailed white paper that highlights how NVIDIA DGX Servers managed by Bright Cluster Manager provide the enhanced technology and support for today’s most demanding AI workloads. This new solution delivers the perfect balance of increased concurrency for handling data science workloads, the massive compute requirements associated with those workloads, and the seamless management of all resources across the HPC cluster.

NVIDIA DGX-1

With the NVIDIA/Bright Solution, HPC Users benefit from:

  • Solid scale-up architecture for handling data-/compute-intensive workloads
  • Easy cluster setup and provisioning
  • End-to-end cluster monitoring, health checking, and automated updates
  • Automated deployment and configuration of HPC workload managers, Kubernetes, machine learning/deep learning frameworks and libraries, and NGC containers
  • Ability to run all the above HPC workloads on the same DGX cluster

As organizations continue to leverage GPUs for HPC applications and also host AI workloads on existing HPC clusters, a growing trend is to add DGX servers to these clusters alongside traditional CPU-based Linux servers. Because Bright Cluster Manager is platform independent, combining DGX servers with servers from another vendor is easy and seamless, resulting in a single unified cluster that Bright can centrally manage and monitor.

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