Accelerated computing for AI in government

AI brings exponential benefits, however, demands careful planning

The government is building momentum in adopting artificial intelligence (AI) and is seeing more use cases go into production as they scale with confidence. Despite these advances, many agencies on the AI journey will soon be confronted with substantial challenges and obstacles – if they haven’t already. At scale, AI often requires access to large data sets, demanding compute resources, speed, and complex networking. As AI implementation gathers pace across government, agencies may find that they do not have ready access to such capabilities and assets.

The challenges arise because the unique computational needs of AI quickly outpace the capabilities of traditional data center architectures. Many AI computations can be done faster and more effectively with GPU-based and other accelerated computing architectures. As a result, designing a plan for an accelerated infrastructure— from the beginning—will help agency leaders maximize the impact of their AI investments.

Government agencies need to design a flexible plan for incorporating accelerated computing in the early stages of AI adoption to plan for scale and success. Government entities can purchase individual workstations for data scientist power-users or set up enterprise data centers. Enterprise servers are ideal for organizations building scalable AI that demands the pinnacle of performance. Good candidates for on-premises accelerated computing include government entities with strict data security regulations, especially those that handle sensitive or confidential information.

Agency leaders have a great opportunity to apply AI to help their organizations. As usage gets more widespread and complex, they are going to need to utilize accelerated computing architecture solutions, such as GPUs and optimized software, to support their AI journey. It is imperative to plan for scale to maximize future success. Planning for these capabilities early will allow for seamless expansion and sophistication of AI use cases.