NVIDIA recently released the 12.0 version of the CUDA Toolkit. This release, which focused on new programming models and CUDA application acceleration through new hardware capabilities, was the first significant update in a long time. After this update, we can now target CUDA custom code, improved libraries, and developer tools that provide architecture-specific features and instructions in the NVIDIA Hopper and NVIDIA Ada Lovelace architectures. NVIDIA's parallel computing platform, CUDA (Compute Unified Device Architecture), was created for general computing and is the main basis for GPGPU. It is a layer of software that gives compute kernels direct access to the virtual instruction set of GPUs as well as parallel computational components.

Two months after their debut sweeping MLPerf inference benchmarks, NVIDIA H100 Tensor Core GPUs set world records across enterprise AI workloads in the industry group's latest tests of AI training. Together, the results show H100 is the best choice for users who demand utmost performance when creating and deploying advanced AI models. MLPerf is the industry standard for measuring AI performance. It's backed by a broad group that includes Amazon, Arm, Baidu, Google, Harvard University, Intel, Meta, Microsoft, Stanford University and the University of Toronto. In a related MLPerf benchmark also released today, NVIDIA A100 Tensor Core GPUs raised the bar they set last year in high performance computing (HPC).