Scaling Everpure NVIDIA Enterprise Reference Architecture with NVIDIA OVX and HGX Servers for Different Deployments

NVIDIA

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Public
Product
FlashBlade
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User Guides
Technology Integrations
NVIDIA
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Documentation

Performance for AI workloads varies depending on the workload type, including training, inference, optimization, and real-time decision making. The following tables provide standard NVIDIA Enterprise Reference Architecture guidance on high-performance storage numbers for various OVX and HGX deployments. Detailed GPU and adapter specifications are outlined in the NVIDIA Enterprise Reference Architecture Whitepaper.

PCIe Optimized 2-4-3-200 Deployment

The PCIe Optimized 2-4-3-200 reference configuration is an Enterprise RA design pattern for scale-out compute nodes, featuring:

  • 2x CPUs for balanced processing

  • 4x PCIe GPUs (eligible GPUs:NVIDIA RTX PRO 6000 Blackwell Server Edition, H100 NVL, H200 NVL and L40S) for accelerated compute

  • 3x network adapters/DPUs (e.g., BlueField-3) for high-speed connectivity

  • 200Gbps network bandwidth per GPU

Use cases include:

  • AI inference: Medium model parameter inference workloads

  • AI training: Small model training and fine-tuning

GPU OVX Nodes

# of FlashBlade//S500

Namespaces

XFMs and uplinks

4

1 chassis

7x2x37TB

1

1 pair, 16 uplinks

8

1 chassis

10x2x37TB

1

1 pair, 16 uplinks

18

2 chassis

10x2x37TB

1

1 pair, 16 uplinks

32

4 chassis

10x2x37TB

1

1 pair, 16 uplinks

PCIe Optimized 2-8-5-200 Deployment

The PCIe optimized 2-8-5-200 reference configuration is a scale-out compute node design, optimized for AI and high-performance workloads. Key features include:

  • 2x CPUs for balanced processing

  • 8x PCIe GPUs (eligible GPUs: NVIDIA RTX PRO Blackwell 6000 Server Edition, H100 NVL, H200 NVL and L40S) for accelerated compute

  • 5x high-speed network adapters/DPUs (e.g., BlueField-3)

  • 200Gbps network bandwidth per GPU

Use cases include:
  • AI inference: Medium model parameter inference workloads

  • AI training: Small model training and fine-tuning

GPU HGX H200 Nodes

# of FlashBlade//S500

(BladesxDFMs)

Namespaces

XFMs and uplinks

4

1 chassis

10x2x37TB

1

1 pair, 16 uplinks

8

1 chassis

10x4x37TB

1

1 pair, 16 uplinks

16

2 chassis

10x4x37TB

1

1 pair, 16 uplinks

32

4 chassis

10x4x37TB

1

1 pair, 16 uplinks

64

8 chassis

10x4x37TB

1

1 pair, 16 uplinks

HGX 2-8-9-400 Reference Configuration

The HGX Reference Configuration 2-8-9-400 is a design pattern for high-performance AI and HPC workloads, featuring:

  • 2x CPUs (e.g., Intel Xeon 8480C PCIe Gen5 or AMD Turin) for balanced processing

  • 8x GPUs (eligible GPUs: HGX H100/H200/B200) with 4th-gen NVLink, delivering 900 GB/s GPU-to-GPU bandwidth

  • 9x network adapters (e.g., BlueField-3) supporting up to 400Gbps per GPU

Use cases include:

  • AI inference: Large (per node) and medium (per GPU) model parameter inference workloads

  • AI training: Large to small model training and fine-tuning based on cluster sizing

GPU HGX Nodes

# of FlashBlade//S500

Namespaces

XFMs and uplinks

4

1 chassis

10x2x37TB

1

1 pair, 16 uplinks

8

1 chassis

10x4x37TB

1

1 pair, 16 uplinks

16

2 chassis

10x4x37TB

1

1 pair, 16 uplinks

32

4 chassis

10x4x37TB

1

1 pair, 16 uplinks

64

8 chassis

10x4x37TB

1

1 pair, 16 uplinks