Tensor Core acceleration of INT8, INT4, and binary round out support for DL inferencing, with A100 sparse INT8 running 20x faster than V100 INT8. New Bfloat16 (BF16)/FP32 mixed-precision Tensor Core operations run at the same rate as FP16/FP32 mixed-precision. For FP16/FP32 mixed-precision DL, the A100 Tensor Core delivers 2.5x the performance of V100, increasing to 5x with sparsity. New TensorFloat-32 (TF32) Tensor Core operations in A100 provide an easy path to accelerate FP32 input/output data in DL frameworks and HPC, running 10x faster than V100 FP32 FMA operations or 20x faster with sparsity. Robust fault isolation allows them to partition a single A100 GPU safely and securely.Ī100 adds a powerful new third-generation Tensor Core that boosts throughput over V100 while adding comprehensive support for DL and HPC data types, together with a new Sparsity feature that delivers a further doubling of throughput. When configured for MIG operation, the A100 permits CSPs to improve the utilization rates of their GPU servers, delivering up to 7x more GPU Instances for no additional cost. The A100 GPU includes a revolutionary new multi-instance GPU (MIG) virtualization and GPU partitioning capability that is particularly beneficial to cloud service providers (CSPs). The A100 GPU enables building elastic, versatile, and high throughput data centers.įigure 1. It adds many new features and delivers significantly faster performance for HPC, AI, and data analytics workloads.Ī100 provides strong scaling for GPU compute and DL applications running in single– and multi-GPU workstations, servers, clusters, cloud data centers, systems at the edge, and supercomputers. The NVIDIA A100 Tensor Core GPU is based on the new NVIDIA Ampere GPU architecture, and builds upon the capabilities of the prior NVIDIA Tesla V100 GPU. Introducing the NVIDIA A100 Tensor Core GPU The NVIDIA accelerated computing platforms are central to many of the world’s most important and fastest-growing industries. In addition, NVIDIA GPUs accelerate many types of HPC and data analytics applications and systems, allowing you to effectively analyze, visualize, and turn data into insights. NVIDIA GPUs are the leading computational engines powering the AI revolution, providing tremendous speedups for AI training and inference workloads. From scaling-up AI training and scientific computing, to scaling-out inference applications, to enabling real-time conversational AI, NVIDIA GPUs provide the necessary horsepower to accelerate numerous complex and unpredictable workloads running in today’s cloud data centers. Such intensive applications include AI deep learning (DL) training and inference, data analytics, scientific computing, genomics, edge video analytics and 5G services, graphics rendering, cloud gaming, and many more. The diversity of compute-intensive applications running in modern cloud data centers has driven the explosion of NVIDIA GPU-accelerated cloud computing. This post gives you a look inside the new A100 GPU, and describes important new features of NVIDIA Ampere architecture GPUs. Today, during the 2020 NVIDIA GTC keynote address, NVIDIA founder and CEO Jensen Huang introduced the new NVIDIA A100 GPU based on the new NVIDIA Ampere GPU architecture.
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