Which method can be employed to ensure high availability within an AI infrastructure?

Implementing load balancing across multiple servers is a fundamental strategy for ensuring high availability in an AI infrastructure. Load balancing distributes incoming traffic and workload across several servers or resources, which helps to prevent any single server from becoming overwhelmed. By doing so, it increases the resilience of the infrastructure; if one server goes down, the load can be redistributed to other operational servers, thereby maintaining service continuity and minimizing downtime. This technique not only improves fault tolerance but also enhances performance by efficiently utilizing available resources, leading to a more robust AI infrastructure capable of handling varying loads effectively.

Other options, while they might improve certain aspects of system performance or reliability, do not directly address high availability in the same way. For example, reducing network latency can improve response times but doesn't inherently prevent downtime if a server fails. Increasing the number of CPUs in a cluster may enhance processing power but, again, does not account for potential server failures that would interrupt service. Maintaining a single point of failure is contrary to the principles of high availability since it creates a vulnerability where the loss of that one component can lead to a complete service outage. Hence, load balancing stands out as the most effective method for ensuring ongoing service availability in an AI infrastructure.