Executable programs and extensions
Do you need an NVIDIA GPU and CUDA for local AI?
If an app tells you to install CUDA, first check whether it actually requires it.
Suppose you open a local language-model app to summarize a document, but its settings show only CPU1. Before installing the CUDA2 Toolkit, check which GPUs and execution paths the app supports. A finished app may include the components it needs, so the Toolkit may not be necessary. If you install PyTorch3 yourself, check its CUDA build and compatible driver; compiling CUDA code may require the Toolkit. Without NVIDIA, you can still run apps that support CPU, Metal4 on Mac, ROCm5 or Vulkan for AMD, or local WebGPU6 in a browser.
When a document app shows only CPU
You open a local language-model app to summarize a document, but its device setting shows only CPU. Does that mean your laptop’s graphics hardware cannot help without CUDA? First check which GPUs and execution paths the app supports. CUDA is a path for NVIDIA GPUs. Running an app and developing CUDA code require different components.
We will use summarizing a company document as the example throughout this guide. If the app does not mention CUDA or shows CPU, that alone is not a reason to install the Toolkit. Check the app’s official guide for supported devices and GPU7 options, then look in its settings or logs for the selected device. Whether the app supports CUDA and whether the PC has CUDA development tools are separate checks.
Tell when an app actually needs CUDA components
A document-summarization app loads the model and offers a setting for its execution device. Tools such as Ollama and desktop apps may install or manage the libraries they need. Follow that project’s setup guide. Many app users do not need to install the CUDA Toolkit separately.
If you process the document with your own PyTorch code, the setup is different. Choose your operating system, installation method, language and compute platform in PyTorch’s official selector to get a matching command. A package may include the CUDA runtime8 libraries, so the system Toolkit may not be needed; the driver and PyTorch CUDA build must still support your GPU and operating system.
You may need the CUDA Toolkit to compile CUDA code or build a CUDA extension; its nvcc command compiles CUDA C/C++. The NVIDIA driver lets the operating system see the GPU and run CUDA work. If the app cannot see the GPU, check the driver and device detection before adding Toolkit components.
The checks report different things. The CUDA Version in nvidia-smi usually describes the level supported by the driver; nvcc --version reports the development Toolkit compiler found on your PATH. The CUDA runtime inside a PyTorch package may differ again. If the numbers do not match, compare what each tool reports; the difference alone does not show that installation failed.
CUDA apps use libraries for specific work, such as cuDNN for deep learning and cuBLAS for linear algebra. Frameworks or apps usually connect the libraries they need. A person using a document-summarization app can follow its setup guide; someone developing GPU software may need to examine CUDA-X components. Use the linked explainer for context, and check the app’s own support list for your GPU’s compatibility.
How can you tell whether the app uses the GPU?
Seeing a GPU in the app’s device list does not mean the whole document summary runs on it. The app must support that GPU backend and model format. Some apps place only part of the model’s layers on the GPU and run the rest on the CPU. Check the GPU offloading9 setting; if the app provides device and layer details in its logs, inspect them for the same summary task.
The model file size alone does not tell you how much will fit on the GPU. VRAM10 also holds the KV cache11 for conversation context and execution buffers. If space runs short, the app may move some work to system RAM12 or the CPU, changing answer time and how many requests it can handle at once. Check the model placement and remaining memory in the app log.
GPU detection does not establish that every model operation is supported. The GPU generation and compute capability, operating system, driver, app build, model data type and kernels must work together. An older GPU may lack newer features or some precision modes. Check minimum drivers and feature limits in NVIDIA’s compatibility guide, then compare your GPU and model with the app’s support list.
Choose a local execution path for this computer
The document app may still run on a laptop without NVIDIA CUDA. If it has an AMD GPU, check whether the app supports ROCm, Vulkan or another backend for that card and operating system. An AMD card does not automatically mean ROCm is supported. Apps differ in supported GPU generations, models, operating systems and drivers, and features can vary by backend within the same app.
Apple Silicon Macs do not run CUDA either. Installing the CUDA Toolkit on a Mac does not turn it into a CUDA PC. To run local models on a Mac, look for apps that support Metal or a path such as MLX13 for Apple Silicon. Developing against a CUDA-only library requires an NVIDIA GPU and operating system supported by that library.
Local execution can use a CPU, Metal on Apple silicon, an AMD backend, or WebGPU in a browser. But a browser app is not necessarily computing locally: if it sends model work to a remote server, the computation is not on your computer. CPU execution can take longer with large models or long contexts, so first match the app’s supported models to the task you want to do.
Verify the setup with the official guide and a small task
Check the document app’s official support list for your operating system, GPU model, backend and minimum driver. A laptop GPU can differ in power and memory from a similarly named desktop card, so compare your laptop’s actual specifications. A model card’s recommendation does not replace the app’s support list.
For an NVIDIA app that requires CUDA, first use nvidia-smi to confirm that the driver sees the GPU, then follow the app’s setup guide. If the operating system cannot see the card, resolve the driver or system configuration before changing app packages. If you install PyTorch yourself, get a command for your current setup from its official selector. A fixed cuXXX address in an old article may not match your Python or driver requirements.
Finally, check the execution path with a small task. Looking at the app’s device information, the framework’s CUDA availability and GPU activity during the task can help distinguish an installation from actual GPU computation. A short task may show low utilization, and VRAM occupancy alone does not prove that computation is happening on the GPU. Read the framework diagnostic alongside the app logs.
Choose software or hardware based on the summary task
If the app sees the GPU and summarizes the same document successfully, you do not need to add the CUDA Toolkit. If it requires CUDA but your GPU or driver does not meet its support requirements, prepare the environment according to the app’s guide. For PyTorch development, choose a framework build and driver with the official selector; add the Toolkit and nvcc when compiling CUDA code.
If the summary is fast enough and the app runs the model you need, keep using your current device. If the app lacks the GPU path you need or the model and context exceed available memory, check the app’s support requirements before comparing hardware changes. A CUDA notice alone is not a reason to buy a GPU or Toolkit.
Terminology notes
CPU — The central processor that executes general-purpose program instructions. AI workloads may divide work between it and other processors such as GPUs.
Back to the textCUDA — A software platform for general-purpose computing on NVIDIA GPUs. Programs built for CUDA are not guaranteed to run unchanged on other GPUs.
Back to the textPyTorch — A software framework for building and running AI models. Check the compatible PyTorch version and hardware support along with the model.
Back to the textMetal — Apple’s low-level technology for graphics and parallel GPU computation. It is not itself a model-selection or chat app.
Back to the textROCm — AMD’s software platform for AI and high-performance computing on GPUs. Support depends on the combination of GPU, operating system, driver, and framework versions.
Back to the textWebGPU — A web-standard API for graphics and general-purpose GPU computation in browsers. Supported features can vary by browser and device.
Back to the textGPU — A processor designed to handle many calculations in parallel. It performs model computations during AI inference.
Back to the textRuntime — The software environment that provides facilities needed while a program runs. In local AI it can also refer to a model execution engine; a GPU runtime library and a complete serving app are different components.
Back to the textOffloading — Moving some model data from GPU memory to system RAM or storage when capacity is limited. This adds data transfer.
Back to the textVRAM — Memory used by a graphics card’s GPU for model weights and intermediate values. It is distinct from system RAM.
Back to the textKV cache — Memory that stores attention keys and values from earlier tokens for reuse during later token generation. Its size depends on context length and batch size.
Back to the textSystem RAM — System memory that temporarily holds data while programs run. It differs from storage and from a discrete GPU’s VRAM.
Back to the textMLX — A machine-learning framework developed by Apple. On Apple silicon it uses unified memory and Metal; separate Linux backends are also available. Model and feature support depends on the MLX-based tool.
Back to the text