Understanding CUDA and RTX: Seven Practical Uses for NVIDIA GPUs
The explainer describes CUDA as NVIDIA's computing platform for sending parallel work to its GPUs and RTX as its graphics and AI technology family. It lists seven practical tasks for NVIDIA hardware, including running a local chatbot, editing photos, processing video, and handling 3D work. The article also notes that different GPU components, such as CUDA cores, Tensor cores, and RT cores, serve different workloads.
CUDA is NVIDIA's framework for directing parallel computations to its GPUs, while RTX describes a broader set of graphics and AI technologies. Inside RTX hardware, CUDA cores handle general parallel math, Tensor cores target AI-related calculations, and RT cores assist virtual light tracing. Video encoding and decoding use separate NVENC and NVDEC engines.
People generally encounter CUDA through applications rather than by coding. A local chatbot, for example, uses a downloaded model and an app; the GPU performs inference. Tools such as ComfyUI can rely on a CUDA-enabled PyTorch build for image generation. Core counts alone do not predict app speed, and acceleration may follow non-CUDA paths.
Wider availability of CUDA/RTX acceleration may affect hobbyists, creative professionals, students, and remote workers by making local AI, photo/video editing, and 3D tasks more practical on personal computers. This could reduce reliance on cloud services for some workflows, potentially improving privacy and latency, while also raising concerns about hardware cost, energy use, and unequal access. Users may need to check app settings, since cloud features can still transmit data.