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A dependable local AI workstation is built in layers: choose an operating system and container workflow, confirm memory and storage, install a host NVIDIA driver, select a compatible CUDA-enabled environment, and then test GPU access with the software you intend to run. NVIDIA AI Workbench can guide that process, but it is optional; other local AI frameworks use their own installation instructions.
The key compatibility rule is simple: the NVIDIA driver runs on the host, while containers or applications use a CUDA version that must be supported by that driver. Treat the driver, CUDA runtime, container runtime, operating system, and AI framework as one stack rather than installing them independently.
Decide what the workstation must run
Write down the models, frameworks, datasets, and user interface you expect to use before installing anything. A workstation for one container and a small model has a different storage and memory profile from a machine that keeps several deep-learning containers, model checkpoints, and datasets locally.
- Workload: Identify whether you need inference, model fine-tuning, training, image generation, speech processing, or development across several frameworks.
- Deployment style: Decide whether applications will run directly on the host or inside containers. Containers make environments easier to reproduce, but each image and its dependencies consume disk space.
- Display role: Decide whether the NVIDIA card will drive a desktop display or operate as a compute-only/headless device. NVIDIA documents different driver configurations for those situations on supported Linux distributions.
- Growth: Leave room for additional models and datasets instead of sizing the disk only for the first container.
These decisions matter because a driver that works for the desktop may still be unsuitable for a selected container if the container’s CUDA version falls outside the driver’s supported range.
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Choose an operating system and container workflow
NVIDIA AI Workbench documents full local installation on the versions below. These are Workbench support statements, not a universal compatibility promise for every NVIDIA GPU, AI framework, or container.
| Operating system | Documented Workbench versions | Workflow note |
|---|---|---|
| Windows | Windows 11, build 22631 or later | Workbench’s local flow uses a container runtime such as Docker Desktop or Podman. |
| Windows | Windows 10 22H2, build 19045.4052 or later | Check the exact framework and GPU requirements in addition to the Workbench version requirement. |
| macOS | macOS 14 or later | Workbench supports the operating-system version; confirm the intended NVIDIA hardware and framework path separately. |
| Ubuntu | Ubuntu 22.04, 24.04, or 26.04 | Workbench documents Ubuntu as the Linux route for native container runtimes without virtualization. |
When Windows is the practical choice
Windows can be appropriate when the machine is also a daily desktop and you want a guided container workflow. Use a supported build, install the host NVIDIA driver, and install the container runtime required by your chosen route. A Windows support entry does not remove the need to verify GPU access inside the container.
When Ubuntu is the practical choice
Ubuntu is the documented native Linux option in Workbench’s matrix. It is a natural fit for a compute-focused machine or a workstation where you are comfortable managing packages, services, drivers, and permissions. Select a desktop driver configuration when the card will drive a monitor; select a compute-only or headless configuration when it will not.
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The Workbench matrix includes macOS 14 or later, but that operating-system statement should not be read as a blanket guarantee for every NVIDIA GPU arrangement or AI framework. Confirm the exact hardware connection, framework support, and container behavior before committing a macOS machine to a GPU-heavy workflow.
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Plan memory, storage, and the GPU
System memory
| Resource | Documented figure | How to interpret it |
|---|---|---|
| System RAM minimum | 16 GB | Workbench’s support matrix minimum for its documented local workflow. |
| System RAM recommended | 32 GB | Workbench’s recommendation; applications, containers, models, and datasets may require more. |
System RAM is separate from GPU memory. The figures above describe host memory for the Workbench workflow, not a universal requirement for every model. A model can fit in GPU memory yet still need substantial host RAM while loading, converting, caching, or preprocessing data.
Storage for containers and data
The full local installation guide asks for 40 GB of free disk space. That is an installation figure, not a complete capacity plan for a long-lived AI workstation. The same documentation illustrates an example PyTorch container that is over 20 GB, while the support matrix estimates approximately 41 GB total for that PyTorch container and its dependencies on Windows and macOS. Those numbers describe one documented configuration and should not be applied to every framework or model.
Budget additional capacity for:
- More than one container image and the layers shared or duplicated between images.
- Model weights, tokenizer files, checkpoints, and converted formats.
- Training or evaluation datasets, temporary caches, and generated outputs.
- Operating-system updates and enough free space for container operations.
An NVMe SSD can be a sensible storage choice for containers, models, and datasets because local AI workloads perform many reads and writes, but the cited NVIDIA documentation does not require NVMe or prescribe a capacity. Choose a drive size from your actual container, model, and dataset inventory.
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Choosing the NVIDIA graphics card
The evidence available for this setup does not establish a best card model, a universal VRAM threshold, a price, or a value winner. Select an NVIDIA graphics card by matching the target model’s memory needs and the framework’s supported GPU architecture, then check power delivery, cooling, physical clearance, and driver availability for the chosen operating system.
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Do not buy on CUDA branding alone. The card, host driver, CUDA version, container image, and framework must form a compatible combination. If you plan to run several workloads at once, account for the aggregate GPU-memory requirement rather than the memory of only the first model.
Install the host NVIDIA driver before testing CUDA
GPU access starts with a working host driver. Installing a CUDA Toolkit does not prove that a driver is installed, and a driver installation does not guarantee that every CUDA container will run.
Ubuntu driver path
NVIDIA AI Workbench can offer to install a missing GPU driver on Ubuntu. It prompts first and does not replace a driver that is already installed. If you manage the system yourself, use NVIDIA’s current Ubuntu driver documentation and select the repository-based configuration appropriate to your Ubuntu release, GPU, and display role.
- Confirm the Ubuntu release and whether the machine needs a desktop display stack or compute-only/headless operation.
- Install the current driver branch recommended for that release and GPU using NVIDIA’s documented repository method.
- Restart when the driver documentation requires it.
- Use the operating system’s NVIDIA status utility to confirm that the card is detected before troubleshooting containers or frameworks.
Package names and supported branches can change, so do not copy an old command intended for a different Ubuntu release.
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Windows and macOS host preparation
Install the NVIDIA driver appropriate to the operating system and card before opening a container or AI project. Keep the host driver under normal system update control, but read the compatibility notes for the CUDA version used by your selected image. The Workbench operating-system support entry alone does not replace a GPU-specific driver check.
The CUDA 13.4 separation
NVIDIA’s CUDA 13.4 Quick Start Guide states: “Starting with CUDA 13.4, the NVIDIA Linux driver is no longer bundled with the CUDA Toolkit and must be installed separately.” Therefore, installing the toolkit on Linux is a second step after establishing the host driver. The same separation applies conceptually to containers: the container supplies its user-space CUDA components, while the host supplies the driver interface.
Choose a local AI software route
Guided route: NVIDIA AI Workbench
Workbench is an optional guided environment for local projects. Its full local installation requires administrator privileges, an internet connection, and a container runtime. The documented flow offers Docker Desktop or Podman and then uses containers for projects such as the example PyTorch environment.
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Manual route
You can instead install the host driver, a container runtime, and the framework or project directly from that project’s documentation. This can give you finer control over image versions, storage locations, networking, and updates, but you must perform the compatibility and verification work yourself. The operating-system and memory figures in the Workbench matrix should not be treated as universal requirements for a manual installation.
Set up the workstation in a safe order
Follow this sequence so that each layer is verified before the next one depends on it.
- Inventory the target workload. Record the model families, framework, expected container images, dataset locations, and whether the GPU will also drive a display.
- Select a supported operating system. If using Workbench, stay within its documented Windows, macOS, or Ubuntu versions and build numbers. If using another framework, check that framework’s support matrix separately.
- Check physical capacity. Confirm system RAM, GPU power and cooling, free disk space, and room for model and dataset growth. Treat 40 GB as the documented Workbench installation allowance, not the final workstation capacity.
- Install the host NVIDIA driver. Choose the desktop or compute-only/headless configuration appropriate to the machine. Reboot if required and verify that the operating system detects the GPU.
- Install the container runtime. For Workbench’s documented flow, use Docker Desktop or Podman as offered for your operating system. Ensure the runtime starts normally before adding large images.
- Install Workbench or your chosen framework. Workbench’s full local flow requires administrator privileges and an internet connection. A manual framework setup follows that project’s own installation sequence.
- Select a CUDA-compatible image or environment. Record the CUDA version supplied by the image and compare it with the host driver’s supported range. Do not assume that the newest image is compatible with an older driver.
- Create or open the project. Keep project files, model caches, and datasets on the storage volume you planned, and monitor available space while the image and dependencies download.
- Run a minimal GPU test. Verify the host first, then verify GPU visibility from inside the intended container or environment before downloading additional models.
- Only then add models and datasets. A successful host test is not proof that the container can access the GPU; complete the end-to-end test with the exact software stack you will use.
Verify GPU access end to end
Host-level verification
Use the NVIDIA driver status utility supplied for your operating system and confirm that the expected card, driver, and basic utilization information are visible. If the utility cannot see the card, stop at the host layer: reinstalling a framework or changing a container will not fix a missing host driver.
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Start the intended container or Workbench project and run its documented GPU check. Confirm that the container sees the NVIDIA device and reports a CUDA version supported by the host driver. A container that starts but silently falls back to CPU is not a successful GPU test.
CUDA sample verification
NVIDIA’s CUDA Quick Start Guide describes installing and running CUDA samples, including building and running a sample executable. Use a sample appropriate to your operating system and toolkit, and treat a successful result as evidence for that host/toolkit combination. Then repeat the check inside the container or framework environment that will run your AI workload.
Troubleshoot the common failure points
The operating system sees no NVIDIA GPU
- Confirm that the card is seated, powered, and enabled in the system firmware when applicable.
- Check that the installed driver matches the operating system release and GPU.
- On Ubuntu, verify whether a desktop or compute-only configuration was installed and consult NVIDIA’s current driver guidance for that release.
- Restart after driver installation when requested, then repeat the host-level check.
The host sees the GPU but the container does not
- Confirm that the container runtime is running and that the environment was configured for NVIDIA GPU access.
- Compare the container’s CUDA version with the host driver’s supported range.
- Test with the exact image used by the project; a host-level CUDA sample does not validate container integration by itself.
- Read the Workbench compatibility guidance when a container cannot access the GPU, rather than installing a second unrelated driver.
The container fails during download or startup
- Check free disk space before retrying. The example PyTorch image alone is over 20 GB, and the documented Windows/macOS example totals about 41 GB with dependencies.
- Remove unused images and caches only after confirming that no project depends on them.
- Verify the internet connection and administrator permissions required by the full local installation.
- Allow additional capacity if you are keeping multiple containers, model files, or datasets.
The workload runs on CPU
First distinguish between a framework configuration issue and a driver or container issue. If the host and container tests pass, inspect the project’s documented device-selection setting and confirm that the model was launched in the GPU-enabled environment. If the container test fails, return to driver/CUDA compatibility rather than changing model settings.
Maintain the stack after installation
Record the operating-system build, NVIDIA driver branch, CUDA version, container image tag, runtime, and project configuration that worked. This makes a future update reversible and helps identify which layer changed when a previously working project fails.
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- Read current NVIDIA driver and CUDA documentation before changing major versions; package branches and supported operating systems evolve.
- Update the host driver and containers deliberately rather than at the same time, so a regression has a clear cause.
- Keep free disk space available for image layers, model downloads, temporary files, and updates.
- Retest both a host CUDA sample and the actual project container after significant driver, runtime, or image changes.
- Recheck framework-specific GPU support whenever you add a new model or switch operating systems.
Pre-flight checklist
- Target models, frameworks, datasets, and display/headless role are defined.
- Operating system and build are supported by the chosen workflow.
- Host RAM meets the workload; Workbench documents 16 GB minimum and 32 GB recommended.
- Storage includes the documented 40 GB Workbench installation allowance plus room for containers, models, and datasets.
- NVIDIA driver is installed for the exact operating system and GPU.
- CUDA version in each container is compatible with the host driver.
- Docker Desktop or Podman is installed when required by the Workbench route.
- Host, container, and intended framework tests all report GPU access.
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