The initial price for the DGX A100 was $199,000.
Nvidia a présenté la carte graphique A100. A new entry has popped up in the Justia trademark database, with NVIDIA filing an application for the DGX A100 trademark.The first DGX systems were named DGX-1 and DGX-2 but it seems that NVIDIA won't be naming the Ampere-based system DGX-3 but rather DGX A100. The system is built on eight NVIDIA A100 Tensor Core GPUs. Costing $199,000, this isn't a system made for gamers. This means that the DGX solution will utilize 1/20th the power and occupy 1/25th the space of a traditional server solution at 1/10th the cost.The system also uses six 3rd-gen NVLink and NVSwitch to make for an elastic, software-defined data center infrastructure, according to Huang, and nine Nvidia Mellanox ConnectX-6 HDR 200Gb per second network interfaces.Of course, unless you’re doing data science or cloud computing, this GPU isn’t for you.
The system features the world’s most advanced accelerator, the NVIDIA A100 Tensor Core GPU, enabling enterprises to consolidate training, inference, and analytics into a unified, easy-to-deploy AI infrastructure that includes direct access to NVIDIA AI experts and multi-layered built-in security.
This document is for users and administrators of the DGX A100 system. NVIDIA delivers the world’s fastest AI training performance among commercially available products, according to MLPerf benchmarks released today.. Destinée au monde professionnel, elle se montre toutefois intéressante pour le particulier. NVIDIA DGX™ A100 offers unprecedented compute density, performance, and flexibility.
Despite coming in at a starting price of $199,000, Nvidia stated that the performance of this supercomputer makes the DGX A100 an affordable solution.
A100 is part of the complete NVIDIA data center solution that incorporates building blocks across hardware, networking, software, libraries, and optimized AI models and applications from NGC ™.Representing the most powerful end-to-end AI and HPC platform for data centers, it allows researchers to deliver real-world results and deploy solutions into production at scale. training and inference infrastructure.
If none of that sounds like enough power for you, Nvidia also announced the next generation of the DGX SuperPod, which clusters 140 DGX A100 systems for an insane 700 petaFLOPS of compute.
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DGX A100. The first DGX systems were named DGX-1 and DGX-2 but it seems that NVIDIA won't be naming the Ampere-based system DGX-3 but rather DGX A100.
nvidia dgx a100 はあらゆる ai ワークフローのためのユニバーサル システムであり、先例のない計算処理密度、パフォーマンス、柔軟性を、nvidia a100 tensor コア gpu に搭載された世界初の ai システムで … This allows administrators to right-size GPUs with guaranteed quality of service (QoS) for multiple workloads.Faster Inference Increases ROI Through Maximized System Utilization
If … Nvidia unveiled its Ampere graphics architecture and its most powerful computer yet: The DGX A100. Training them requires massive compute power and scalability.Highest versatility for all workloads.BERT Large Inference | NVIDIA TensorRT™ (TRT) 7.1 | NVIDIA T4 Tensor Core GPU: TRT 7.1, precision = INT8, batch size = 256 | V100: TRT 7.1, precision = FP16, batch size = 256 | A100 with 1 or 7 MIG instances of 1g.5gb: batch size = 94, precision = INT8 with sparsity.BERT Large Inference | NVIDIA T4 Tensor Core GPU: NVIDIA TensorRT™ (TRT) 7.1, precision = INT8, batch size = 256 | V100: TRT 7.1, precision = FP16, batch size = 256 | A100 with 7 MIG instances of 1g.5gb: pre-production TRT, batch size = 94, precision = INT8 with sparsity.To unlock next-generation discoveries, scientists look to simulations to better understand complex molecules for drug discovery, physics for potential new sources of energy, and atmospheric data to better predict and prepare for extreme weather patterns.Customers need to be able to analyze, visualize, and turn massive datasets into insights.
DGX A100: DGX A100 with 8X A100 using INT8 with Structural Sparsity.With MIG, the eight A100 GPUs in DGX A100 can be configured into as many as 56 GPU instances, each fully isolated with their own high-bandwidth memory, cache, and compute cores.
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