The Challenges of Building a Platform for AI
Data scientists depend on computing performance to gain insights and innovate faster, using the power of deep learning and analytics. GPU technology offers a faster path to AI, but building a platform goes well beyond deploying a server and GPU’s. AI and deep learning can require a substantial commitment in software engineering. An investment that could delay your project by months as you integrate a complex stack of components and software including frameworks, libraries, and drivers. Once deployed, additional time and resources are continually needed as you wait for the ever-evolving open source software to stabilize. You’ll also be waiting to optimize your infrastructure for performance, along with administrative costs that increase as the system scales.
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.