The Tool Desk
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Choose a setup path before installing
Decide what you need the environment to do. A native runtime is usually the shortest path to a local endpoint for application code. WSL2 suits Windows developers who need Linux tools. Docker can isolate a runtime, but GPU access adds platform-specific requirements. Building a runtime from source is for developers who need to select or modify its backend—not a prerequisite for running a model locally.
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- Identify your system: note whether you are using Windows or Linux, the exact GPU vendor and model, and your available system memory, GPU memory, and disk space.
- Start with the runtime: use a native installation unless your tooling or deployment needs call for Linux in WSL2 or a container.
- Verify support before configuring a GPU: GPU compatibility depends on the device, driver, operating system, and backend. Do not assume that a working GPU on one path will work in another.
Ollama is one option for this setup, not the only local inference runtime. The instructions below use it because its Windows, Linux, API, and development documentation describe these paths.
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Install and start with the native Windows runtime
- Install Ollama using its official Windows installer.
- Open Ollama and use its command-line or app workflow to download and run a model. Model choice and resource use vary; check the requirements for the specific model and workload rather than treating a general RAM or VRAM figure as universal.
- Keep the runtime running while you develop. Ollama’s Windows documentation shows a local API request to
http://localhost:11434/api/generate, which application code can use to send generation requests.
For a PowerShell smoke test, the following sends a request to the documented endpoint. Enter the name of a model you have already downloaded when prompted; the request body illustrates the generate call, not the full API contract.
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$model = Read-Host "Downloaded model name"
$prompt = Read-Host "Prompt"
$body = @{ model = $model; prompt = $prompt; stream = $false } | ConvertTo-Json
Invoke-RestMethod -Method Post -Uri "http://localhost:11434/api/generate" -ContentType "application/json" -Body $bodyFor supported fields, response formats, streaming behavior, and other API operations, use Ollama’s current API documentation. This local endpoint is intended for development on the machine; the documentation cited here does not establish a complete configuration for safely exposing it to other machines.
Where Windows keeps files and logs
Ollama’s Windows documentation identifies %HOMEPATH%.ollama for models and configuration and %LOCALAPPDATA%Ollama for logs. If the service appears to start but a request fails, inspect the logs as part of diagnosis.
How do I install Ollama on Linux?
Follow Ollama’s current Linux installation instructions for your distribution, then use the documented systemd service controls. The specific installation procedure can change, so consult the current Linux guide rather than copying an old package or setup command.
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- Install Ollama according to the current Linux guide.
- Start the service:
sudo systemctl start ollama. - Check whether it is active:
sudo systemctl status ollama. - If the service is not behaving as expected, inspect its journal:
journalctl -e -u ollama. - Once the service is running, use the local runtime from your development workflow and configure your application against its local API.
These service and journal commands are documented by Ollama’s Linux guide. A successful service status confirms the service state; it does not by itself prove that a particular model is downloaded, that a GPU is in use, or that a request will produce the output you expect.
Should I use native Windows, WSL2, or Docker?
Choose based on your toolchain and isolation needs. The paths are not interchangeable when GPU access matters.
| Path | Best fit | GPU and operational considerations |
|---|---|---|
| Native Windows | Windows tools and the simplest Windows setup. | Use the runtime’s supported Windows GPU path for your exact device and driver; otherwise, begin without assuming acceleration. |
| WSL2 | Windows developers who need a Linux toolchain. | GPU access depends on the Windows, WSL2, driver, runtime, and container configuration. Follow the specific GPU guide rather than assuming all combinations work. |
| Docker Desktop on Windows | Developers who want container isolation and a documented NVIDIA GPU container path. | Docker’s Ollama GPU guide requires a current NVIDIA driver and Docker Desktop’s WSL2 backend for this Windows path. The guide describes GPU support for containers on Linux and Windows 11; do not read that as a guarantee for every Windows/Docker combination. |
| Docker Engine on native Linux | Linux developers who need the Ollama runtime in a container. | For the NVIDIA container flow described by Docker, install NVIDIA Container Toolkit. Docker’s guide recommends running Ollama outside a container when using Docker Desktop on a Linux machine. |
If you do not need container isolation or Linux-specific tools, adding WSL2 and Docker creates extra components without being a requirement for local inference. Conversely, a container that cannot see the intended GPU may use a different execution path than you expect, so confirm device access within the chosen setup.
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How should I add GPU acceleration?
First confirm that the exact GPU and operating system are supported by the backend you intend to use. A GPU’s vendor name alone is not enough to determine compatibility: driver installation, runtime version, backend, and device family matter.
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For the NVIDIA GPU container flow in Docker’s Ollama guide, use Docker Desktop with its WSL2 backend and a current NVIDIA driver on Windows. On native Linux with Docker Engine, the guide names NVIDIA Container Toolkit as a prerequisite. These are distinct platform instructions; do not apply the Windows Desktop steps to native Linux or assume a Linux container has GPU access merely because the host does.
AMD and ROCm
For AMD acceleration with llama.cpp, consult AMD’s ROCm compatibility information for the supported device family and the ROCm version you plan to use, and install the matching GPU driver separately. AMD documents Ubuntu and Windows configurations, but the correct route depends on the selected GPU and ROCm version. Do not install a generic ROCm package on the assumption that every AMD GPU is supported.
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If you need a source-built AMD ROCm environment, AMD’s installation material describes a prebuilt Docker image as a way to avoid installation issues and also documents a manual build path. Treat either as an advanced option, not the default first step.
Building Ollama from source
Ollama’s build documentation lists CUDA, ROCm, and Vulkan backend options. Choose the backend that matches your platform and hardware rather than enabling every option. The Windows build instructions also call for the Visual Studio Native Desktop workload. A source build is useful when you need development control; it is unnecessary just to establish a basic local runtime.
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On Windows, Ollama documents the OLLAMA_MODELS environment variable for choosing where downloaded model files are stored. Set it in your user environment to the desired model directory, then restart Ollama so the running process can use the updated setting. Use a directory on the other drive with sufficient available space.
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Changing the model directory is a storage and organization choice. It does not make inference faster, and the documentation does not give a universal disk-capacity recommendation. Keep model files distinct from the runtime installation when that makes backups or disk management easier.
How do I check whether my local LLM server is running?
- On Linux: run
sudo systemctl status ollama. If the service is inactive or fails, inspectjournalctl -e -u ollama. - On Windows: use the local API endpoint shown in Ollama’s Windows documentation,
http://localhost:11434/api/generate, to make a request from PowerShell or application code. A successful response shows that a request was handled; it does not establish GPU use. - Check the model: confirm that the model named in the request has been downloaded and that the request uses the current API format.
- Check acceleration last: if output works but performance or device use is unexpected, verify the selected GPU backend, driver compatibility, and whether the process or container can access the intended device.
Ollama identifies Windows logs under %LOCALAPPDATA%Ollama; Linux service output can be inspected with the journal command above. Checking service state and logs before changing model prompts helps separate runtime problems from model or application issues.
What to do when setup fails
- Confirm that the runtime process or service is active.
- Read the platform logs for startup or request errors.
- Verify that the requested model is present and named correctly.
- If you expect GPU acceleration, check supported device, driver, backend, and operating-system combinations.
- For Docker, confirm that the container path meets the relevant host prerequisites and that the intended GPU is exposed to the container.
This order is a practical way to isolate common setup layers, not a guarantee that every failure has the same cause. Local model output and performance also depend on the model, its configuration, and the machine.
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