In terms of processing speed, what does a distributed processing model achieve?

A distributed processing model enhances processing speed significantly by dividing tasks across multiple machines or nodes. This approach allows for parallel execution of processes, meaning that multiple operations can occur simultaneously rather than sequentially. Each node works on its portion of the workload, leading to a reduction in the overall time required to complete tasks.

By distributing the processing load, the system can leverage the combined computational power of multiple devices, which is particularly beneficial for processing large datasets or complex computations that would take a considerable amount of time if handled by a single machine. This capability to execute tasks concurrently is a crucial advantage of distributed systems in applications ranging from data analysis to web services and computational simulations.

The other options suggest potential limitations or drawbacks of distributed processing, which do not capture its primary strength in improving efficiency and speed.