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Optimized through the DGX software stack and NVIDIA CUDA-X data center solution, it targets faster time-to-solution and improved ROI with flexible deployment options. 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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.  \nThe Universal System for Every AI Workload  \nNVIDIA DGX™ A100 is the universal system for all AI workloads—from analyticsto training to inference. DGX A100 sets a new bar for compute density, packing 5 peta FLOPS 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 ( MIG) capability in the NVIDIA A100 Tensor Core GPU, which enables administrators to assign resources that are right-sized for specific workloads. Available with up to 640 gigabytes (GB) of total GPU memory, which increases performance in large-scale training jobs up to 3X and doubles the size of MIG instances , DGX A100 can tackle the largest and most complex jobs , 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 Slur m and Kubernetes clusters deployed with NVIDIA DeepOps .  \nDirect Access to NVIDIA DGXperts  \nNVIDIA DGX A100 is more than a server. It ’s a complete hardware and software platform built upon the knowledge gained from the world’s largest DGX proving ground—NVIDIA DGX SATURNV—and backed by thousands of DGXperts at NVIDIA. DGXperts are AI-fluent practitioners who offer prescriptive guidance and design expertise to help fastrack AI transformation. They 've built a wealth  \nSYSTEM SPECIFICATIONS  \n\n| NVIDIA DGXA100\u003Cbr>640GB |  | NVIDIA DGXA100\u003Cbr>320GB |\n| --- | --- | --- |\n| GPUs 8x NVIDIA A100\u003Cbr>80 GB GPUs |  | 8x NVIDIA A100\u003Cbr>40 GB GPUs |\n| GPU Memory 640 GB total |  | 320 GB total |\n| Performance | 5 petaFLOPS AI\u003Cbr>10 petaOPS INT8 |  |\n| NVIDIA\u003Cbr>NVSwitches | 6 |  |\n| System Power Usage | 6.5 kW max |  |\n| CPU | Dual AMD Rome 7742 , 128 cores total, 2. 25 GHz (base), 3.4 GHz (max boost) |  |\n| System Memory | 2 TB | 1 TB |\n| Networking | 8x Single- | 8x Single- |\n|  | Port Mellanox | Port Mellanox |\n|  | ConnectX-6 VPI | ConnectX-6 VPI |\n|  | 200Gb/s HDR | 200Gb/s HDR |\n|  | InfiniBand | InfiniBand |\n|  | 2x Dual-Port | 1x Dual-Port |\n|  | Mellanox | Mellanox |\n|  | ConnectX-6 VPI | ConnectX-6 VPI |\n|  | 10/25/50/100/200 | 10/25/50/100/200 |\n|  | Gb/s Ethernet | Gb/s Ethernet |\n| Storage | OS: 2x 1.92 TB M.2 | OS: 2x 1.92TB M.2 |\n|  | NVME drives | NVME drives |\n|  | Internal Storage: | Internal Storage: |\n|  | 30 TB (8x 3 .84 TB) | 15 TB (4x 3 .84 TB) |\n|  | U.2 NVMe drives | U.2 NVMe drives |\n| Software | Ubuntu Linux OS\u003Cbr>Also supports:\u003Cbr>Red Hat Enterprise Linux\u003Cbr>CentOS |  |\n| System Weight | 271.5 lbs (123.16 kgs) max |  |\n| Packaged System Weight | 359.7 lbs (163.16 kgs) max |  |\n| System Dimensions | Height: 10.4 in (264.0 mm) Width: 19.0 in (482.3 mm) max\u003Cbr>Length: 35.3 in (897.1 mm) max |  |\n| Operating Temperature Range | 5–30 ºC (41–86 ºF) |  |\n\n | DATA SHEET | NOV20  \nof know-how and experience over the last decade to help maximize the value of your DGX investment. DGXperts help ensure that critical applications get up and running quickly , and stay running smoothly , for dramaticallyimproved time to insights . ","cbCaio3BtVi3j71f","https://ap.wps.com/l/cbCaio3BtVi3j71f","pdf",282440,"English","# System Specifications\n## Compute, Memory, and Performance\n## Networking and Storage\n## Software and Operating Environment\n# Key Capabilities\n## Scaling Enterprise AI Workloads\n## Fastest Time to Solution\n## Enterprise Security\n## Data Center Scalability","[{\"question\":\"What problem does NVIDIA DGX A100 aim to solve for enterprise AI scaling?\",\"answer\":\"DGX A100 addresses the complexity, higher costs, limited speed of scale, and siloed compute architecture typical of traditional AI infrastructure, by unifying analytics, training, and inference workloads in a single platform.\"},{\"question\":\"How does Multi-Instance GPU (MIG) help manage different AI workloads?\",\"answer\":\"MIG enables administrators to allocate computing resources in a fine-grained, right-sized way for specific workloads, improving flexibility while supporting both large and smaller jobs.\"},{\"question\":\"What security approach does DGX A100 provide for enterprise AI?\",\"answer\":\"DGX A100 uses a multi-layered security posture across major hardware and software components, covering areas such as baseboard management controller (BMC), CPU/GPU boards, self-encrypted drives, and secure boot.\"}]","NVIDIA DGX A100 - The Universal System for AI Infrastructure - Datasheet | PDF"]