NVIDIA DGX A100
The Challenge of Scaling Enterprise AI
Every business needs to transform using artificial intelligence (AI), not only to survive, but to thrive
in challenging times. However, the enterprise requires a platform for AI infrastructure that improves upon
traditional approaches, which historically involved slow compute architectures that were siloed by analytics,
training, and inference workloads. The old approach created complexity, drove up costs, constrained speed of scale,
and was not ready for modern AI. Enterprises, developers, data scientists, and researchers need a new platform
that unifies all AI workloads, simplifying infrastructure and accelerating ROI.
The Universal System for Every AI Workload
DGX A100 sets a new bar for compute density, packing 5 petaFLOPS of AI performance into a 6U form factor, replacing
legacy compute infrastructure with a single, unified system. DGX A100 also offers the unprecedented ability to deliver
fine-grained allocation of computing power, using the Multi-Instance GPU capability in the NVIDIA A100 Tensor Core GPU,
which enables administrators to assign resources that are right-sized for specific workloads. This ensures that the
largest and most complex jobs are supported, along with the simplest and smallest. Running the DGX software stack with
optimized software from NGC, the combination of dense compute power and complete workload flexibility make DGX A100
an ideal choice for both single node deployments and large scale Slurm and Kubernetes clusters deployed with NVIDIA DeepOps.
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SPECIFICATIONS
GPUs
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8x NVIDIA A100 Tensor Core GPUs
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GPU Memory
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320 GB total
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Performance
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5 petaFLOPS AI
10 petaOPS INT8
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NVIDIA NVSwitches
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6
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System Power Usage
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6.5kW max
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CPU
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Dual AMD Rome 7742, 128 cores total, 2.25 GHz (base), 3.4 GHz (max boost)
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System Memory
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1 TB
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Storage
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OS: 2x 1.92TB M.2 NVME drives
Internal Storage: 15TB (4x 3.84TB) U.2 NVME drives
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Network
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8x Single-Port Mellanox ConnectX-6 VPI
- 200Gb/s HDR InfiniBand
1x Dual-Port Mellanox ConnectX-6 VPI
- 10/25/50/100/200Gb/s Ethernet
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