What action would best improve efficiency and performance in an AI data center?

Distributing AI workloads across multiple servers with GPUs while using DPUs to manage network traffic is an effective strategy for improving efficiency and performance in an AI data center. This approach allows for parallel processing of AI tasks, leveraging the hardware capabilities of multiple GPUs to enhance throughput and reduce processing time.

Using multiple servers allows for better load balancing, as tasks can be spread out based on resource availability and demand. This not only prevents any single server from becoming a bottleneck but also enhances resource utilization, leading to more responsive and resilient system performance.

Furthermore, integrating DPUs into this architecture helps optimize network management, freeing up CPUs and GPUs to focus on computational tasks. This division of labor ensures that data can flow smoothly between the compute and storage resources without overwhelming the CPU, which is essential in data-intensive AI workloads.

In contrast, consolidating workloads onto a single server may lead to inefficiencies due to resource contention, and while allocating networking tasks exclusively to CPUs or relying solely on CPUs for training will not take full advantage of specialized AI hardware, which is designed to accelerate specific AI computations. Using CPUs only for background operations does not optimize the overall architecture and can lead to performance limitations as well.