The World's First Deep Learning Supercomputer In a Box
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.
The NVIDIA® DGX-1™ is the world's first purpose-built system for deep learning with fully integrated hardware and software that can be deployed quickly and easily. Its revolutionary performance significantly accelerates training time, making the NVIDIA DGX-1 the world's first deep learning supercomputer in a box.
Revolutionary AI Performance
While many solutions offer GPU-accelerated performance, only NVIDIA DGX-1 unlocks the full potential of the latest NVIDIA GPU’s like the NVIDIA® Tesla® V100, including innovations like next generation NVLink and new Tensor Core architecture.
With its performance-engineered deep learning software stack, DGX-1 delivers up to three times faster training speed than other GPU-based systems. With the computing capacity of 25 racks of conventional servers in a single system that integrates the latest NVIDIA GPU technology with the world’s most advanced deep learning software stack, you can take advantage of revolutionary performance to gain insights faster than ever, powered by NVIDIA DGX-1.
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-1 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.
NVIDIA DGX-1 removes the burden of continually optimizing your deep learning software and delivers a ready-to-use, optimized software stack that can save you hundreds of thousands of dollars. It includes access to today’s most popular deep learning frameworks, NVIDIA DIGITS™ deep learning training application, third-party accelerated solutions, the NVIDIA Deep Learning SDK (e.g. cuDNN, cuBLAS, NCCL), CUDA® toolkit, NVIDIA Docker and NVIDIA drivers.