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You can run local AI on Linux with an NVIDIA GPU, but there is no single installation command that fits every distribution, GPU, and AI runtime. First confirm that Linux can see your GPU and that its driver is compatible with the software you plan to use. Then choose a runtime for your goal—such as an approachable local model runner, a development framework, or an API-serving stack—and follow that runtime’s current Linux instructions.
- Identify your Linux distribution and exact NVIDIA GPU.
- Install a compatible NVIDIA driver using the instructions for your distribution and GPU.
- Choose one runtime and check its supported model formats, GPU requirements, and installation instructions.
- Estimate whether your GPU has enough memory for the model and workload you want.
Understand the components before installing them
A local AI setup can involve several layers, but you do not necessarily install each one separately:
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- NVIDIA driver: lets Linux and applications communicate with the GPU.
- CUDA toolkit and runtime libraries: provide NVIDIA GPU computing components. The toolkit includes development components; an application may instead use a packaged runtime or a container.
- Framework: software such as PyTorch used to build or run machine-learning code.
- Inference runtime: software that loads and runs a model, such as Ollama, llama.cpp, vLLM, SGLang, or TensorRT-LLM.
- Model and interface: model weights plus the way you use them, such as a local interactive workflow or an API service.
These pieces are related but not interchangeable. In particular, the host driver, CUDA toolkit, and framework or runtime have separate compatibility requirements. NVIDIA’s CUDA 13.4 Linux guide says the toolkit and driver are independently versioned; its cuda-toolkit package installs toolkit components, not the driver. Check the current CUDA Installation Guide for Linux for your distribution and GPU instead of assuming one toolkit or driver combination applies to all systems.
The CUDA 13.4 guide lists Ubuntu 22.04 LTS, 24.04 LTS, and 26.04 LTS among the supported distributions on that page. Support changes over time, and a distribution listed for CUDA is not automatically supported by every AI runtime. The guide documents distribution-specific Debian/RPM package methods and a distribution-independent runfile method. Its Debian/Ubuntu example, apt install cuda-toolkit, is not complete repository setup and is not a universal instruction; follow the guide’s complete steps if your chosen workflow requires a host toolkit.
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Choose a runtime for the job
NVIDIA lists PyTorch, Ollama, llama.cpp, TensorRT-LLM, SGLang, and vLLM among local inference backends. It advises selecting based on operating system, model format, GPU architecture and memory, API requirements, and throughput target. The table is a starting point, not a compatibility guarantee; verify the current Linux instructions and requirements for the specific release you intend to install. NVIDIA’s Local AI overview lists these backend options.
| Option | Best fit | Check before installing |
|---|---|---|
| Ollama | An approachable local model workflow. | Its current Linux instructions, GPU support, and model availability for your setup. |
| llama.cpp | Running models supported by its formats and quantization route. | Model-file compatibility, GPU support, and whether the desired quantization fits available memory. |
| PyTorch | Developing or running framework code. | The generated install command for your platform and whether your code expects a particular CUDA-enabled build. |
| vLLM or SGLang | Serving-oriented workloads, including API use where supported by the chosen stack. | Release-specific hardware, model, and serving requirements; do not assume identical support between the two. |
| TensorRT-LLM | NVIDIA-optimized LLM inference when its engine-building workflow and version constraints suit the project. | The exact release’s Linux prerequisites, CUDA and PyTorch constraints, and installation method. |
PyTorch’s local installation selector generates an install command from the preferences you choose. PyTorch describes Stable as its most currently tested and supported release; Preview builds are nightly and less tested. Use the selector rather than copying a command from an older guide.
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Install in a safe, path-specific order
- Record your system details. Note the Linux distribution and release, the exact NVIDIA GPU, and the model or workload you want. Confirm that the runtime’s current guide covers your combination.
- Install a compatible host driver. Use the applicable distribution or NVIDIA instructions. Reboot if that procedure requires it.
- Check GPU visibility. Confirm that the operating system and driver recognize the GPU before troubleshooting a framework or runtime. If they do not, resolve the driver or device-visibility problem first.
- Pick one runtime. Follow its current official Linux instructions, including any required packaged libraries, toolkit, environment, or container. Do not add a host CUDA toolkit just because a guide mentions CUDA; first determine whether that workflow requires one.
- Run that runtime’s own smoke test. Use the test documented for the installed runtime and release. There is no single test command established here that applies across all of these stacks.
For a container workflow, installing the host driver alone does not configure Docker to expose the GPU. NVIDIA’s current Container Toolkit installation guide documents this Docker configuration sequence after the toolkit is installed:
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sudo systemctl restart dockerThe first command updates Docker’s configuration to use the NVIDIA runtime; the second restarts Docker. Use the guide for the selected container engine and the full installation procedure. Containers still rely on a working host driver.
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Account for version coupling in specialized stacks
PyTorch
Use the current PyTorch selector for the platform and package variant you need. A CUDA-related error can result from a mismatch between the installed PyTorch build and the selected platform, so verify the build before changing the system driver or adding toolkit packages.
TensorRT-LLM
TensorRT-LLM is more than a generic model launcher: NVIDIA describes it as a Python API for defining LLMs and building TensorRT engines, with Python and C++ runtimes to execute those engines. Its Linux pip instructions are release-specific. The page accessed for this guide says it was tested on Ubuntu 24.04 and specifies CUDA Toolkit 13.1 and a PyTorch CUDA 13.0 package for that page; those values should not be transplanted into a different release’s setup. The same page warns that pip can replace an existing PyTorch installation and lead to runtime errors. Read the current TensorRT-LLM Linux pip instructions for the exact release, and consider its documented NGC development-container alternative if that better fits your environment. NVIDIA’s TensorRT-LLM documentation describes the engine and runtime model.
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Size the model to available GPU memory
Check the GPU’s VRAM and the workload’s performance needs before choosing a model. A model that loads is not necessarily a good fit for your target context length, concurrent users, or response speed. NVIDIA’s local AI guidance suggests Q4_K_M checkpoints for llama.cpp and NVFP4 for vLLM or PyTorch as options to consider, not universal best choices. Quantization can reduce memory needs, but the right trade-off depends on the task, model, runtime, and acceptable output quality.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Evaluate candidate models on the work you actually expect them to do. NVIDIA recommends using a custom dataset and human grading rather than treating a format or quantization label as a quality guarantee. Its Local AI page also states an “Over 100M local NVIDIA GPU installed base” figure; that is NVIDIA’s company-stated figure, not an independently verified market estimate.
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Troubleshoot by locating the failing layer
- GPU is missing: start with driver installation and device visibility in Linux. A runtime cannot use a GPU that the host does not expose.
- PyTorch reports CUDA unavailable: check that the installed PyTorch build matches the platform and installation choice selected on PyTorch’s current local page.
- Packages conflict: use a clean isolated environment or a documented container rather than repeatedly layering incompatible packages onto an existing setup.
- TensorRT-LLM fails after installation: compare the installed release with its own Linux prerequisites and PyTorch constraints; pip may have replaced a pre-existing PyTorch package.
- Docker cannot access the GPU: check that NVIDIA Container Toolkit is installed and configured for the selected engine, and that the engine was restarted after configuration.
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