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You can run local AI on Linux with an NVIDIA GPU, but there is no single installation recipe that fits every distribution, card, model, and runtime. Start by identifying your GPU and Linux version, confirm that the NVIDIA driver can see the card, then choose a runtime for your goal: an approachable local model runner, framework development, or API serving. Install only the components that path requires; a full host CUDA Toolkit is not mandatory for every workflow.
What you need before installing a local AI runtime
- An NVIDIA GPU and a Linux distribution supported by the driver and runtime you intend to use.
- A working NVIDIA driver, with the GPU visible to Linux.
- Enough free disk space for the runtime and model files. Model size and storage needs vary, so check the model you plan to use.
- A clear goal: interactive local use, development with a framework, or serving requests through an API.
Check current compatibility rather than assuming that a GPU or Linux release is supported just because another NVIDIA setup works. NVIDIA’s local AI overview identifies operating system, model format, GPU architecture and memory, API needs, and throughput goals as factors in choosing a backend.
Understand which parts of the software stack you need
- NVIDIA driver: lets Linux and applications communicate with the GPU. It is a host component, including when the AI application runs in a container.
- CUDA Toolkit and runtime libraries: provide GPU development components and libraries used by some software. A packaged runtime or container may supply what an application needs; not every workflow requires a separately installed host toolkit.
- Framework: a development layer such as PyTorch, used to run or build framework-based code.
- Inference runtime: software that loads a model and performs inference, such as Ollama, llama.cpp, vLLM, SGLang, or TensorRT-LLM.
- Model and interface: the weights or checkpoint you run and the way you interact with it, such as a local application or an API.
Keep driver, toolkit, framework, and runtime versions distinct. The CUDA 13.4 Linux guide documents the toolkit and driver as separately versioned and installed; its cuda-toolkit package installs toolkit components, not the driver. Its supported-distribution list includes Ubuntu 22.04 LTS, 24.04 LTS, and 26.04 LTS, but compatibility can change: check the current CUDA Linux installation guide for your distribution and GPU. The guide covers distribution-specific package installation and a runfile approach; a package example is not a universal repository setup or a recommendation for every system.
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Choose a runtime for your workload
| Option | Best fit | What to check before installing |
|---|---|---|
| Ollama | An approachable local model workflow. | Check the current Linux installation instructions and whether the model and GPU fit your intended use. |
| llama.cpp | Working with its supported model formats and quantized checkpoints. | Confirm the required format, GPU support, and quantization choices for your specific model. |
| PyTorch | Running or developing code that uses the PyTorch framework. | Select the operating system, package, and compute platform on PyTorch’s local install page; use the generated command for that selection. |
| vLLM or SGLang | Serving-oriented use cases where API requirements and throughput matter. | Read the chosen project’s current Linux quickstart and compatibility requirements; do not assume their install steps or supported configurations are interchangeable. |
| TensorRT-LLM | NVIDIA-optimized LLM inference when its engine-building workflow and version constraints are appropriate. | Check the exact release’s Linux instructions, prerequisites, and compatibility matrix before installing. |
NVIDIA lists these backends and frames the choice around model format, GPU architecture and memory, API needs, and throughput target. Use its backend overview as a shortlist, then follow the selected runtime’s official instructions. The options are not interchangeable: they differ in supported model inputs, installation work, and the kind of workload they address.
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Install in a sequence that avoids mismatched components
- Identify your system. Record your Linux distribution and release, exact GPU, and available VRAM. Check the driver and runtime documentation for compatibility before changing packages.
- Install a compatible NVIDIA driver. Follow the procedure for your distribution and GPU in its official instructions. Reboot if that procedure requires it.
- Verify host GPU visibility. Confirm that Linux and the NVIDIA driver can see the card before debugging an AI framework or model. If the device is not visible at this stage, resolve the driver or device issue first.
- Choose one runtime and follow its current Linux instructions. For PyTorch, use the selectors on the PyTorch local installation page and copy the generated command. PyTorch describes Stable as its most currently tested and supported release; Preview/nightly is less tested. Avoid copying old commands because the available packages and selections can change.
- Run that runtime’s own smoke test. Use the test documented by the chosen project to confirm it can use the GPU, then try the model or workload you actually intend to run. There is no single test command that applies to all the backends above.
Do not add a host CUDA Toolkit by default. The CUDA guide explains toolkit and driver installation separately, and a runtime may package or otherwise provide the libraries it needs. If your selected project’s instructions require a particular toolkit, install the version specified for that project and release.
If you choose Docker, configure GPU access separately
A container does not remove the host-driver requirement. NVIDIA’s Container Toolkit instructions configure the Docker runtime so containers can use the NVIDIA GPU. After installing and configuring the toolkit as directed for your system, run the documented Docker configuration and restart Docker:
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sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart dockerThe first command updates Docker’s configuration to use the NVIDIA runtime; the second restarts Docker. Follow the NVIDIA Container Toolkit installation guide for the full, current setup and any prerequisites. Container configuration is an additional step, not a substitute for a working host driver.
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- The GPU is missing: check host driver installation and device visibility before changing model or runtime settings.
- PyTorch reports CUDA unavailable: verify that the installed PyTorch build matches the platform and compute selection used to generate its install command. Recheck the current PyTorch local installation options.
- Packages conflict: isolate the environment or use a documented container path rather than layering incompatible package sets into an existing environment.
- TensorRT-LLM fails during installation or at runtime: compare the error with the prerequisites for the exact TensorRT-LLM release. Its Linux pip page warns that pip can replace an existing PyTorch installation, which can cause runtime errors.
TensorRT-LLM’s Linux pip page currently says it was tested on Ubuntu 24.04 and gives version-specific CUDA Toolkit and PyTorch package details, alongside an NGC development-container alternative. Those values belong to that page’s instructions, not to every TensorRT-LLM release. Consult the release-specific Linux installation page before installing. TensorRT-LLM is a Python API for defining LLMs and building TensorRT engines, with Python and C++ runtimes for executing them; see the TensorRT-LLM documentation for its role and current details.
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Match model size and quantization to VRAM and quality needs
Choose a model only after checking the GPU’s available VRAM and the performance you need. A model that fits may still be too slow for your intended use, while reducing memory use can affect output quality. Leave room for the runtime, context, and other GPU workloads rather than treating the card’s advertised memory as entirely available to model weights.
NVIDIA suggests Q4_K_M checkpoints for llama.cpp and NVFP4 for vLLM or PyTorch as options to consider; these are vendor recommendations, not guarantees that a particular quantization will be best for every model or task. Evaluate the actual workload with a custom dataset and human review, as NVIDIA recommends. Its local AI guidance also describes checking VRAM and performance requirements before shortlisting models.
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If your current card cannot meet the memory or throughput needs of your intended workload, hardware with more VRAM may be one option, but an upgrade is not a prerequisite for every local AI task. Select hardware against a specific model, runtime, and use case rather than a generic claim that a certain GPU is required.
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