A dependable local AI workstation follows this order: choose a supported operating system and container workflow, reserve enough memory and disk space, install the host NVIDIA driver, confirm that your CUDA version is compatible with that driver, install a container runtime and local AI environment, then test GPU access with the software you intend to use. NVIDIA AI Workbench can guide this process on supported Windows, macOS and Ubuntu systems, but it is optional; the same principles apply to a manually assembled stack.
Decide what you are building before installing anything
“Local AI workstation” can mean several different things: running inference in a container, developing a PyTorch application, experimenting with several models, or operating a headless compute server. Your intended workload determines how much storage, memory, cooling and administration work you need. The documentation available for this setup does not establish a universal GPU model, VRAM threshold, performance ranking or price winner, so select the graphics card only after checking the target model or framework’s own requirements.
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An NVIDIA graphics card is central to this workflow because the host driver provides GPU access to applications and containers. Check the card’s support in the exact framework, driver branch and operating system you plan to use. Also account for power delivery, case clearance, cooling and whether the machine must drive a desktop display. A card used only for compute can be configured differently from one that also runs the monitor.
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Choose an operating system and container approach
| Operating system | Documented AI Workbench support | Container-workflow note |
|---|---|---|
| Windows 11 | Build 22631 or newer | Use the documented local-install flow and its supported container runtime. |
| Windows 10 | 22H2, build 19045.4052 or newer | Verify the current support matrix before committing to a new installation. |
| macOS | Version 14 or newer | Confirm that the intended framework and GPU arrangement are supported; Workbench support does not guarantee every AI stack. |
| Ubuntu | 22.04, 24.04 or 26.04 | The documented Linux route for native container runtimes without virtualization. |
These are NVIDIA AI Workbench support statements, not a promise that every framework, model or NVIDIA card works identically on every listed release. Ubuntu is the clearest choice when you want a native Linux container runtime without a virtualization layer. Windows and macOS can be convenient desktop systems, but their container and GPU integration should be checked against the particular software you intend to run.
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Plan RAM, storage and physical capacity
Memory
The AI Workbench support matrix lists 16 GB of system RAM as the minimum and 32 GB as the recommended amount. Those figures describe Workbench’s documented baseline, not a ceiling for local AI. Running several services, a browser, an editor, containers and a dataset at once can require more. Check the framework and model documentation when your workload is larger than a single development container.
Disk space
The full local Workbench installation asks for 40 GB of free disk space because deep-learning containers are large. Its example PyTorch container is more than 20 GB, and the support matrix gives an approximately 41 GB total example for that PyTorch configuration on Windows and macOS. These are examples for a particular installation, not a universal capacity recommendation.
Budget beyond that baseline for additional containers, model weights, caches, source trees, datasets, logs and future updates. On Ubuntu, container space is stored directly on the filesystem; make sure the filesystem containing the runtime has enough free capacity rather than assuming another drive will be used automatically. An NVMe SSD can be a sensible storage choice for large containers and model files, but the cited NVIDIA documentation does not require NVMe or specify a capacity.
Power, cooling and display use
GPU acceleration does not remove ordinary workstation constraints. Verify that the power supply, motherboard slot, case and cooling solution can support the selected card. If the machine will run as a server, decide whether the card is compute-only or also drives a display. NVIDIA’s Ubuntu driver documentation describes desktop and compute-only/headless configurations; select the configuration that matches the machine’s role instead of installing a graphical stack on a headless host by habit.
Choose a guided or manual software route
NVIDIA AI Workbench as a guided route
AI Workbench documents a full local installation for the operating-system versions in the table above. It can organize projects and containers and, on Ubuntu, can offer to install a missing GPU driver. The prompt appears before installation and does not replace a driver that is already installed. The documented flow requires administrator privileges, an internet connection and a container runtime; Docker Desktop and Podman are the runtime choices shown in that flow.
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A manually assembled stack
Workbench is not required for local AI. You can install the operating system, host driver, container runtime, framework and model tools separately. This gives you more control over versions and service layout, but you must resolve compatibility yourself. In either route, the host driver remains a prerequisite for GPU access, and the container’s CUDA version must be compatible with that driver.
| Route | Best fit | Responsibility you retain |
|---|---|---|
| AI Workbench | A guided project and container setup on a supported desktop or Ubuntu system. | Confirm the support matrix, provide storage and RAM, choose a runtime, and resolve driver or container compatibility errors. |
| Manual installation | Users who need a custom runtime, framework versions or server layout. | Install and maintain every host, driver, CUDA, runtime and framework component. |
Follow the installation sequence
1. Prepare and update the host operating system
- Install one of the supported operating-system releases, or update an existing installation to a documented build.
- Apply normal operating-system updates and reboot before adding the GPU stack.
- Confirm that you have administrator or root access. The documented full local Workbench install requires elevated privileges.
- Connect the machine to the internet for installer downloads, container images, dependencies and updates.
- Reserve at least 40 GB of free space for the documented Workbench local install, then add capacity for every model, dataset and additional container you expect to keep.
Do not treat the Workbench matrix as a universal compatibility list. If you plan to use a different framework, check that framework’s operating-system, GPU and driver support before proceeding.
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The Workbench local-install flow requires a container runtime and documents Docker Desktop or Podman. Install the runtime appropriate to your operating system, start its service, and verify that your user can launch a basic container before adding AI software. On Linux, decide whether you want a native runtime; Ubuntu is the documented Workbench Linux route for that arrangement without virtualization.
Keep the runtime’s image and volume storage location in mind. A runtime can consume the system disk even when your source code is stored elsewhere. Redirect storage only after confirming that the destination filesystem has sufficient free space and appropriate permissions.
3. Install the host NVIDIA driver
The driver belongs on the host operating system, outside the container. It is what exposes the GPU to the runtime and applications. A CUDA toolkit inside a container cannot substitute for a working host driver.
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On Ubuntu, AI Workbench can offer to install a missing driver during its setup. If you install the driver separately, use NVIDIA’s current Ubuntu driver instructions and repository options for your exact distribution, GPU and use case. Those instructions distinguish desktop configurations from compute-only or headless configurations. Package names and driver branches change, so follow the current procedure rather than copying an old command from an unrelated release.
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4. Match CUDA to the host driver
Every CUDA-using container has a CUDA version that must be compatible with the host driver. NVIDIA’s AI Workbench support matrix states: “Container CUDA versions must be compatible with host GPU drivers.” A container may therefore fail even when the GPU is physically installed and the driver appears in the operating system.
Do not confuse the CUDA toolkit with the driver. NVIDIA’s CUDA 13.4 Quick Start Guide explicitly says: “Starting with CUDA 13.4, the NVIDIA Linux driver is no longer bundled with the CUDA Toolkit and must be installed separately.” Installing a toolkit, or selecting a container tagged with a CUDA release, is not proof that the host driver is present or sufficiently new.
Before choosing a project image, record the CUDA version it expects and compare that requirement with the driver branch supported by your host. If you need a newer CUDA release, update the host driver through the supported operating-system procedure rather than assuming the container can upgrade it for you.
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5. Install AI Workbench or your chosen development tools
- Download the current installer for your operating system from NVIDIA’s AI Workbench documentation.
- Run it with administrator privileges and allow it to complete its prerequisite checks.
- When prompted for a container runtime, select the installed Docker Desktop or Podman setup documented for your platform.
- On Ubuntu, review the driver prompt carefully. Allow Workbench to install a missing driver only if that is the configuration you want; it is designed not to replace a driver already installed.
- Create or open a project that uses the framework and CUDA version you have checked against the host driver.
- Let the initial container and dependency downloads finish before judging the installation. The example PyTorch image alone is over 20 GB, and the documented Windows/macOS example totals about 41 GB.
If you are not using Workbench, install the framework and project tooling only after the host driver and runtime are working. Keep framework dependencies inside the project environment or container so that changing one project does not silently replace another project’s versions.
6. Verify host GPU access
Start with a host-level check. Run:
nvidia-smiThe command should identify the NVIDIA GPU and report a driver version. If it fails, stop at the host layer: a container cannot repair a missing, unloaded or incompatible driver.
Next, use the CUDA Quick Start procedure for your platform to build and run one of its CUDA samples. A successful sample confirms more than a visible PCIe device: it checks that the compiler or sample binary can communicate with the installed CUDA-capable driver. Follow the guide’s build instructions for the sample you choose because commands differ by sample and operating system.
Finally, test the actual container or project you will use. Confirm that the runtime grants the container GPU access, that the framework detects the card, and that the container’s CUDA version is within the host driver’s supported range. A host-level success with a project-level failure usually points to runtime GPU passthrough, image compatibility or permissions rather than to the physical card.
Diagnose common failures in the order they occur
The operating system is unsupported
Check the exact release and build, not only the marketing name. Windows 11 must meet the documented 22631-or-newer requirement; Windows 10 must be 22H2 build 19045.4052 or newer; macOS must be version 14 or newer; and the listed Ubuntu choices are 22.04, 24.04 and 26.04. Upgrade or choose a supported release before troubleshooting containers.
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The GPU is not visible on the host
- Run
nvidia-smioutside the container. - Confirm that the card is seated, powered and enabled by the operating system.
- Check that only the intended driver branch is installed.
- On Ubuntu, verify whether you installed a desktop or compute-only/headless configuration appropriate to the machine.
- Reboot when the driver procedure requires it, then repeat the host check.
The container cannot access the GPU
- Confirm the host-level driver check succeeds first.
- Verify that the container runtime is running and that GPU access is enabled for it.
- Compare the image’s CUDA version with the host driver’s compatibility range.
- Read the Workbench support matrix entry for the operating system and runtime combination instead of assuming that another platform’s instructions apply.
The CUDA toolkit appears installed, but applications still fail
Separate the toolkit from the host driver. CUDA 13.4 and later on Linux require a separately installed driver, and an installed toolkit does not establish driver compatibility. Recheck the host driver, then the container’s CUDA requirement.
The installation runs out of disk space
Measure free space on the filesystem used by the container runtime, not only the drive containing your source code. The documented 40 GB Workbench baseline can be consumed quickly by one large image; the example PyTorch container is over 20 GB, and additional models and datasets need their own space. Remove unused images and caches only after confirming that no active project depends on them, or relocate runtime storage to a larger disk.
Performance is unexpectedly poor
First establish that the intended framework actually detects the GPU. Then check power, cooling, driver compatibility and whether the workload is accidentally running on the CPU. The available documentation does not provide independent benchmarks or a model-specific performance ranking, so do not infer a card’s suitability from a generic CUDA installation alone.
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Keep the workstation maintainable
Record the working combination
Write down the operating-system release, GPU model, host driver version, container runtime, project image and CUDA version after the first successful test. This makes a later upgrade diagnosable and prevents accidentally mixing a project image with a driver branch it does not support.
Reserve capacity for growth
One container is only the starting point. Multiple framework versions, quantized and full-precision model files, checkpoints, datasets and logs can each consume substantial space. Keep project data separate from disposable caches where practical, and monitor the filesystem used by the runtime.
Update one layer at a time
Change the operating system, host driver, runtime and project image separately. After each change, repeat the host GPU check and then the project-level test. If an update breaks access, the last changed layer is a useful starting point for rollback or compatibility review.
Quick Recap
What a successful setup looks like
- The operating system matches a documented support release for your chosen workflow.
- The machine has at least 16 GB of RAM, with 32 GB as the documented Workbench recommendation.
- The runtime filesystem has at least 40 GB free for the documented Workbench local install, plus room for containers, models and data.
nvidia-smiidentifies the host GPU and driver.- A CUDA sample builds and runs using the installed stack.
- Your selected container can access the GPU.
- The container CUDA version is compatible with the host driver.
- The desktop or headless driver configuration matches how the workstation will be used.
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