The fastest path to deep learning

Data scientists and artificial intelligence (AI) researchers require accuracy, simplicity, and speed for deep learning success. Faster training and iteration ultimately means faster innovation and faster time to market. Building a platform for deep learning goes well beyond selecting a server and GPUs. A commitment to implementing AI in your business involves carefully selecting and integrating complex software with hardware. NVIDIA® DGX A100 fast-tracks your initiative with a solution that works right out of the box, so you can gain insights in hours instead of weeks or months.

Effortless productivity

Today’s deep learning environments can cost hundreds of thousands of dollars in software engineering hours, and months of delays for the open source software to stabilize. With NVIDIA DGX A100 you’re immediately productive, with simplified workflows and collaboration across your team. Save time and money with a solution that’s up-to-date with the latest NVIDIA optimized software.

Revolutionary ai performance

While many solutions offer GPU-accelerated performance, only NVIDIA DGX A100 unlocks the full potential of the latest NVIDIA® Tesla® V100, including next generation NVIDIA NVLink™, and new Tensor Core architecture. DGX A100 delivers 3X faster training speed than other GPU-based systems by using the NVIDIA GPU Cloud Deep Learning Stack with optimized versions of today’s most popular frameworks.

Iterate and innovate faster

High-performance training accelerates your productivity, which means faster time to insight and faster time to market.

Game Changing Performance

TRAINING

DLRM Training

Up to 3X Higher Throughput for AI Training on Largest Models

DLRM on HugeCTR framework, precision = FP16 | 1x ​DGX A100 640GB batch size = 48 | 2x DGX A100 320GB batch size = 32 | 1x DGX-2 (16x V100 32GB) batch size = 32. Speedups Normalized to Number of GPUs.

INFERENCE

RNN-T Inference: Single Stream

Up to 1.25X Higher Throughput for AI Inference

MLPerf 0.7 RNN-T measured with (1/7) MIG slices. Framework: TensorRT 7.2, dataset = LibriSpeech, precision = FP16. ​

Big Data Analytics Benchmark

RNN-T Inference: Single Stream

Up to 83X Higher Throughput than CPU, 2X Higher Throughput than DGX A100 320GB

Big data analytics benchmark | 30 analytical retail queries, ETL, ML, NLP on 10TB dataset | CPU: 19x Intel Xeon Gold 6252 2.10 GHz, Hadoop | 16x DGX-1 (8x V100 32GB each), RAPIDS/Dask | 12x DGX A100 320GB and 6x DGX A100 640GB, RAPIDS/Dask/BlazingSQL​. Speedups Normalized to Number of GPUs ​

EXPLORE THE POWERFUL COMPONENTS OF DGX A100