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Why Developing and Deploying AI Technology on Workstations Makes Sense
AI has taken off as an important, differentiating capability in all industries, and the hardware required to run AI is rapidly evolving. Many businesses are working on AI initiatives that do not require a supercomputer, and instead require powerful workstations.
Get this analyst report from IDC for an understanding of what the requirements for AI deployment really look like, and how Dell's portfolio of workstations play an important role.
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Why are workstations preferred for AI development?
Workstations provide several benefits for AI development, including immediate availability and proximity to data, which enhances productivity. They allow data scientists to experiment freely without the constraints of server access or accumulating cloud costs. Additionally, workstations are often more affordable than servers, with a one-time cost that avoids the ongoing expenses associated with cloud instances.
How is AI deployment evolving at the edge?
Workstations are increasingly being used for AI inferencing at the edge, where they can operate effectively without high-end GPUs. This trend is driven by the need for data-intensive workloads to be processed closer to where data is generated. IDC projects that organizations will invest $6.9 billion in AI compute at the edge by 2026, highlighting the importance of workstations in this evolving landscape.
What are the stages of AI development?
The AI development process consists of six key stages: data management, data preparation, model selection, model development, model training, and model hosting and monitoring. Workstations can support each of these stages by providing the necessary computational power and flexibility, allowing data scientists to manage and experiment with AI models efficiently throughout the development lifecycle.
Why Developing and Deploying AI Technology on Workstations Makes Sense
published by Divergent IT
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