NVIDIA A100

AI models are becoming increasingly complex. Training these models requires massive computational power, as well as flexible capabilities to scale. NVIDIA A100 Tensor Cores with Tensor Float (TF32) provide up to 20x the performance compared to NVIDIA Volta without code changes, and an additional 2x increased efficiency with automatically mixed precision and FP16.

For state-of-the-art conversational AI models like BERT, the A100 accelerates inference with up to a 249x improvement in throughput compared to CPUs. Multi-instance GPU (MIG) technology enables the simultaneous operation of multiple networks on a single A100 for optimal use of compute resources. The support for structural sparsity provides up to 2x increased efficiency – in addition to the other A100 GPU's performance gains in inferences.

In a benchmark for Big Data analytics, the A100 80GB delivered an 83x increased throughput compared to CPUs and a 2x increase over the A100 40GB. This makes the A100 80GB the ideal choice for new workloads with exploding data volumes.