NVIDIA A100 HBM Ampere GPU 80GB premiere the world's fastest memory bandwidth at over 2 terabytes per second to run the largest simulation models and datasets. It allows researchers to quickly deliver accurate results and deploy solutions into production at scale. NVIDIA A100 Tensor Cores with Tensor Float (TF32) provide up to 20x higher performance over the NVIDIA Volta with zero code changes and an additional 2x boost with automatic mixed precision and FP16. For the largest models with enormous data tables like deep learning recommendation models (DLRM), Ampere A100 80GB GPU reaches 1.3 TB of unified memory per node and delivers up to a 3x throughput increase over A100 40GB GPU. In MLPerf, it has set multiple performance records in the industry-wide benchmark for AI training.

For many tasks, such as deep learning (also known as deep structured learning or hierarchical learning), a CPU is no longer enough. In these cases, a GPU will actually help you perform operations significantly faster. This is primarily because with a modern GPU you'll be able to run many more threads, a common CPU may have 16 cores while a common GPU has over 4000 cores. These cores are much simpler and cannot do as much a s CPU, but they don't have to in this case, meaning a GPU allows you to train your neural network much faster. Steadfast GPU dedicated servers can support a wide array of operating systems, such as Windows and most Linux distributions, and thus support most known machine learning and neural network libraries (i.e.