CUDA: strengths, limits and the best alternatives

Graphics processing units (GPUs) have become essential for artificial intelligence, simulations and data analysis. The vast majority of GPUs in use worldwide are made by NVIDIA – and on almost all of them runs the company’s own platform, CUDA. But what exactly hides behind those four letters, where does CUDA have the edge, and what alternatives exist?

What is CUDA and how does it work?

CUDA stands for Compute Unified Device Architecture. Behind this abbreviation lies a platform and programming interface (API) that makes it possible to use GPUs directly for general-purpose computing, going far beyond their original role in graphics. Developers can extend standard code in C/C++ or Fortran with GPU-specific functions and distribute the work across thousands of processing cores at once. This speeds up demanding projects – from fluid dynamics simulations to training deep learning models.

The strengths of CUDA

High performance on NVIDIA hardware
CUDA is tailored to NVIDIA GPU architecture and can take full advantage of their massive parallelism. This leads to top performance in many areas, such as complex scientific simulations or the training of large language models.

Mature tool stack and large community
Since its launch in 2007, CUDA has been steadily developed. It is easier to get started with than many open alternatives, thanks to a large developer community, comprehensive documentation, countless tutorials and active support from NVIDIA.

Strong integration with AI frameworks
Almost all major AI frameworks, including PyTorch and TensorFlow, use CUDA as their default accelerator. CUDA users can also access optimised libraries such as cuDNN, cuBLAS and cuFFT.

Straightforward programming
CUDA provides a well-documented API and tools that make GPU programming easier than with OpenCL or Vulkan. NVIDIA also offers developer tools like Nsight Compute, Systems and Graphics for code optimisation and debugging.

Proven standard in science and industry
Whether molecular dynamics in chemistry, image recognition in medicine or financial simulations: CUDA has established itself as a classic in GPU computing across many disciplines.

The limits of CUDA

CUDA code only runs on NVIDIA GPUs. Developers who need platform independence, or who want to use AMD, Intel or Apple GPUs, must take different paths.

Unlike OpenCL or SYCL, CUDA programmes cannot easily be ported to other architectures without modification. For standard linear algebra routines, it usually makes little sense to write custom CUDA code. Established libraries like cuBLAS or MAGMA are more efficient. Similarly, for I/O-heavy applications with little numerical workload, the benefits of CUDA are limited.

What alternatives exist?

• OpenCL: Open standard, runs on many GPU and CPU architectures. Good for platform-independent projects, but more complex to code.
• SYCL: Based on C++, it promises portable code across different hardware.
• HIP (AMD): Particularly relevant if existing CUDA code needs to be ported to AMD GPUs.
• Vulkan Compute or Numba (Python): Useful for specific real-time or high-level applications.

When it makes sense to use alternatives

• Edge devices and mobile: For ARM (without NVIDIA GPU), Apple or Qualcomm chips, CUDA is not available. For ARM devices with NVIDIA GPUs (e.g. Jetson for robotics or autonomous vehicles), CUDA can be used. See: developer.nvidia.com/arm.
• Long-term hardware independence: Open standards make it easier to switch GPU vendors.
• Mixed compute architectures: When projects must support several types of hardware simultaneously, OpenCL or SYCL are usually the best options.

CUDA or not? A quick decision guide