![]() The ability to lay claim to leading edge tools is a very significant motivator for this group, and should not be underestimated. This is why many find their way into various hyper-scale enterprises, and large academic institutions who have built massive AI supercomputers that house petaFLOPS of computing power. But a key dimension that attracts and retains these individuals, is the desire to accomplish their life’s work, using the most leading-edge tools for AI exploration. The global pool of AI talent is small, and these highly fought-over experts are enjoying the mantle of commanding top salaries and choosing the workstyle and environment that meets their demands. These combined benefits result in lower TCO when you consider the on-going OpEx beyond the initial CapEx outlay, making the ROI of a fully integrated AI workstation a more attractive proposition.Īttracting the Best AI Talent with the Tools They Prefer ![]() ![]() This ensures a continuous lifecycle of productivity from desk-side to data center. NVIDIA DGX Systems enable this workflow, with models constructed on a DGX Station effortlessly ported to an NVIDIA DGX-1 in the data center, since these platforms share the same containerized version of optimized deep learning frameworks. When the time comes, this transition should be ideally effortless, with the painstaking work done at the developer desk scaled-up on a data center server. The Move from Productive Experimentation to Training-at-ScaleĪt some point in your deep learning journey comes the need to operationalize training at scale in the data center. By contrast, DGX Station offers a plug-in, power-up deployment experience that allows developers to start experimenting in just a couple hours, and offers enterprise-grade support, with full-stack troubleshooting, and access to a team of deep learning experts who stand behind the product. ![]() This effort can represent weeks or months of effort, and hundreds of thousands of dollars in software engineering expertise.įinally, once the platform has been validated and is operational, developers may find themselves scouring community forums and consulting multiple hardware and software vendors when problems arise (for example, when a framework is updated or when a component fails).Īll of these impacts drive up the solution OpEx in terms of man-hours of effort, well beyond the initial cost of the componentry. They’ll also spend significant effort working with open source deep learning frameworks, modifying code to try and optimize the software for the configured hardware. When compared with an integrated hardware and software solution like NVIDIA DGX Station, DIY approaches mean your valued developers and innovators will spend an extensive amount of time playing the role of “systems integrator”, specifying and assembling discrete pieces of hardware and software, followed by troubleshooting any incompatibilities. While the initial outlay of building a “do-it-yourself” (DIY) AI workstation seems lower than a purpose-built platform that’s pre-integrated, there are several impacts associated with this approach. Gien that GPU technology is so readily accessible, many enthusiasts feel compelled to proceed with building a platform for deep learning on their own, intending to save time and money. Purpose-built vs Build-it-Yourself GPU Workstations NVIDIA DGX Station is a new breed of GPU workstation aka “AI Workstation” designed to help organizations accelerate through this “productive experimentation” phase, enabling them to embark on production-scale deep learning training faster, and deriving insights sooner, thereby accelerating the ROI of AI. This process demands practitioner freedom and flexibility to work on their terms, without being constrained to a centralized, shared resource, and without having to negotiate with IT on the tools they need to be productive at this early stage, especially when GPU computing resources may not be pervasively accessible in their environment. ![]() The Role of AI Workstations in the Enterpriseįor every team setting out to use AI to transform their business, their journey begins with painstaking, repetitive experimentation and iteration, as their developers learn about various deep learning frameworks, build their skills, grow their datasets, and begin to create models. These solutions play a pivotal role in increasing developer productivity, accelerating time-to-insights, attracting AI talent to your organization, and improving the ROI of your investment in deep learning In this article, we explore the justification of purpose-built AI workstations in the enterprise. ![]()
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