The article also outlines challenges in deploying AI inference workloads, such as the immaturity of applications, volatility in request handling, and the need for significant backup when deploying at the edge. Current inference workloads are often handled by large AI IT clusters initially designed for training, which may not be the most efficient use of resources. As AI evolves, it will require more computing power, and providers are exploring ways to make models more efficient. The article envisions a future where accelerated IT stacks evolve, optimizing power use in larger data centers.
Key takeaways:
- AI's value is realized during the inference stage, where optimized workloads should use minimal IT resources and power.
- AI inference workloads are currently being handled by large AI IT clusters, which are often overkill for the task.
- Deploying AI inference at the edge requires significant backup power and infrastructure due to its business-critical nature.
- Inference AI is evolving rapidly, with future models requiring more computing power and efficient deployment strategies.