cc42e4fa7e54cbfb3c8fd2442b28b25fe97d0207676b53c35ba3e8f41559069b

1. **Parallelism and speed:** GPUs run many operations in parallel, so they are usually much faster for large neural network inference. CPUs have fewer cores and are better for smaller or more sequential workloads.

2. **Cost and resource use:** GPU inference often needs dedicated VRAM and more power, but gives higher throughput. CPU inference can run on more common hardware with lower setup cost, but is usually slower for large models.