Why Developing and Deploying AI Technology on Workstations Makes Sense
Organizations that want to develop and deploy AI may be concerned about costs, security and flexibility. For organizations doing massive algorithms with billions of parameters, servers may be the answer. But for most businesses and organizations, AI development and deployment does not require expensive, accelerated server hardware. Workstations are a better choice. Download this thorough white paper by International Data Corporation (IDC), a global provider of market intelligence, describing the benefits of workstations for a useful, impactful and manageable answer.
Why are workstations preferred for AI development?
Workstations provide several benefits for AI development, including immediate availability, powerful GPU acceleration, and lower costs compared to servers. They allow data scientists to experiment freely without the anxiety of accumulating cloud costs and ensure sensitive data remains securely stored on-premises.
How is AI deployment evolving at the edge?
Workstations are increasingly vital for AI inferencing at the edge, often performing tasks without the need for high-end GPUs. Use cases include AIOps, telehealth, and traffic management. IDC predicts that spending on AI compute at the edge will grow from $2.9 billion in 2023 to $6.9 billion by 2026.
What are the stages of AI development?
The key stages of AI development include data management, data preparation, model selection, model development, model training, and model hosting and monitoring. Workstations can support all these stages by providing the necessary computational power and flexibility, allowing for efficient experimentation and deployment.
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