CUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing framework and API that enables developers and researchers to write GPU-accelerated applications. It provides libraries, compilers, and runtime tools for leveraging NVIDIA GPUs to accelerate compute-intensive workloads in machine learning, scientific simulation, data processing, and visualization. CUDA is a framework dependency that ships as part of the development kit and is used by applications and libraries (PyTorch, TensorFlow, MATLAB, scientific codes) that need GPU acceleration on NVIDIA hardware.
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1 suggestionCUDA is fundamentally an NVIDIA-specific framework designed for NVIDIA GPUs running on x86_64 and Linux/Windows architectures. Apple Silicon Macs do not have NVIDIA GPUs — they feature Apple's integrated GPU as part of the System-on-Chip. NVIDIA has never released a native CUDA implementation for macOS or Apple Silicon, and there is no indication they plan to do so. If you have CUDA installed on your Mac, it is likely a legacy Intel-era installation that will stop working when Apple removes Rosetta 2 in macOS 28 (September 2028). For AI and GPU acceleration on Apple Silicon, Apple and the developer community have standardized on Metal Performance Shaders (MPS), MLX, and similar Apple-native frameworks instead. If you are developing or running machine learning workflows, migrate to frameworks that natively support Apple Silicon (PyTorch with MPS backend, TensorFlow with Metal plugin, MLX, or Ollama). If you must use CUDA-dependent code, you will need access to an NVIDIA GPU on a Windows or Linux machine, or consider cloud-based alternatives with NVIDIA hardware.
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