Many agencies can use their existing central processing unit (CPU)-based platforms for AI workloads without purchasing specialized GPUs. Replacing aging data center components with the latest generation servers and processors offers agencies a significant boost in computing power and energy efficiency. Refreshing a five-year-old server with the latest processors can qatar rcs data lower total ownership cost by 77% by reducing the number of servers needed for the same performance, according to IDC research. Consolidating workloads on fewer servers and cores can also lower software licensing costs and shrink the data center’s overall footprint and operating expenses.
AI accelerators are part of what makes this possible, as it is essentially like boosting a car’s performance with nitrous oxide. These CPUs are architected for advanced computing with higher memory and data throughput capabilities, making them especially well-suited for tasks like AI inference. GPUs, on the other hand, fall short in the memory specs. Their strength is processing tasks in parallel, such as training AI models. However, most AI workloads – roughly 80% – center on inference.
Developments in AI are always rapidly moving so it can be challenging to know where to start with AI adoption. There’s no well-trodden playbook for the public sector to follow. Agencies’ first impulse is often to build their version to maintain control of sensitive data and enforce security requirements. However, this approach is often incredibly time-consuming and expensive while also lagging behind the capabilities developed in the private sector.