NVIDIA

GPU Computing for AI: Parallel Processing and Performance Graphics processing units (GPUs) deliver massive parallel power for AI. Instead of one fast CPU core, a modern GPU runs thousands of threads that work on different parts of a workload at the same time. For AI, most tasks are matrix multiplications and tensor operations, which GPUs handle very efficiently. Two main forms of parallelism drive AI systems: data parallelism and model parallelism. Data parallelism splits a batch across devices, so each GPU computes gradients on its slice and then averages results. Model parallelism divides the model itself across GPUs when a single device cannot fit all layers. Many setups combine both to scale training. ...