GPU acceleration

GPU acceleration offloads massively parallel tasks (like matrix math) from the CPU to the GPU's thousands of specialized cores, delivering performance speedups often exceeding 10x.

This technology fundamentally re-architects computation: it shifts data-parallel workloads from the CPU’s few, powerful cores to the GPU’s many, smaller cores. The result is a dramatic increase in throughput for concurrent operations (e.g., training a massive AI model). Key frameworks like NVIDIA CUDA and OpenCL enable this offloading, transforming fields like High-Performance Computing (HPC), deep learning, and scientific simulation. For instance, a complex molecular dynamics simulation that takes hours on a CPU cluster can complete in minutes on an NVIDIA A100 Tensor Core GPU.

https://www.nvidia.com/en-us/deep-learning-ai/