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NVIDIA DGX™ A100 is the universal system for all AI workloads, offering unprecedented compute density, performance, and flexibility in the world’s first 5 petaFLOPS AI system. NVIDIA DGX A100 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.
A100 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.
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. For FP16/FP32 mixed-precision DL, the A100 Tensor Core delivers 2.5x the performance of V100, increasing to 5x with sparsity.
New Bfloat16 (BF16)/FP32 mixed-precision Tensor Core operations run at the same rate as FP16/FP32 mixed-precision. Tensor Core acceleration of INT8, INT4, and binary round out support for DL inferencing, with A100 sparse INT8 running 20x faster than V100 INT8. For HPC, the A100 Tensor Core includes new IEEE-compliant FP64 processing that delivers 2.5x the FP64 performance of V100.
The NVIDIA A100 GPU is architected to not only accelerate large complex workloads, but also efficiently accelerate many smaller workloads. A100 enables building data centers that can accommodate unpredictable workload demand, while providing fine-grained workload provisioning, higher GPU utilization, and improved TCO.
The NVIDIA A100 GPU delivers exceptional speedups over V100 for AI training and inference workloads as shown in Figure 2. Similarly, Figure 3 shows substantial performance improvements across different HPC applications.
While many data center workloads continue to scale, both in size and complexity, some acceleration tasks aren’t as demanding, such as early-stage development or inference on simple models at low batch sizes. Data center managers aim to keep resource utilization high, so an ideal data center accelerator doesn’t just go big—it also efficiently accelerates many smaller workloads.
The new MIG feature can partition each A100 into as many as seven GPU Instances for optimal utilization, effectively expanding access to every user and application.