Workstations provide several benefits for AI development, including immediate availability and proximity to data, which facilitates experimentation. They allow AI scientists to work without negotiating server time, offer GPU acceleration even when server-based GPUs are scarce, and represent a one-time expense rather than ongoing cloud costs. Additionally, workstations ensure that sensitive data is securely stored on-premises, alleviating concerns about data privacy.

The edge is becoming a rapidly growing scenario for AI deployment, with organizations investing $2.9 billion in AI compute at the edge in 2023, projected to reach $6.9 billion by 2026. Workstations are increasingly used for AI inferencing at the edge, often without the need for high-end GPUs. This shift allows businesses to process data closer to its source, improving efficiency and reducing latency, which is particularly beneficial for applications in sectors like healthcare, manufacturing, and disaster response.

Using workstations for AI development can be more cost-effective than cloud services, which can lead to significant expenses due to metered resource consumption. For instance, the cost of a single virtual machine with necessary resources can escalate to $2,700 per month for full-time training. In contrast, a high-end workstation typically incurs a lower one-time cost, allowing for unrestricted experimentation without the pressure of accumulating cloud bills.