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Here are two differences between CPU and GPU inference.
One key difference is the parallelism level. CPUs are excellent at sequential tasks and handling a few threads efficiently, whereas GPUs excel at massively parallel computations, making them ideal for tasks like large matrix multiplications common in deep learning inference. Another difference is the memory hierarchy and bandwidth. CPUs typically have a smaller, but highly complex cache hierarchy optimized for low-latency access to small data, while GPUs are designed with massive memory bandwidth to feed thousands