Yes—but the supported arrangement is a Windows host connected to the DGX Station GB300 through a qualified Windows Subsystem for Linux (WSL) environment, not a native Windows installation replacing the Station’s operating system. NVIDIA ships the current GB300 DGX Station with Ubuntu 24.04 and NVIDIA AI Developer Tools. Its documented Windows route is an ARM64 WSL configuration described in NVIDIA’s NemoClaw system-readiness guidance. A generic Windows installation, an x86-64 WSL distribution, or an older DGX Station should not be assumed to have the same GPU support.
The practical workflow is to identify the Station generation, confirm the exact ARM64 WSL and GPU combination, verify that the GPU is visible inside Linux, then select a model runtime such as Ollama, vLLM, SGLang, PyTorch, or Jupyter. NVIDIA names these tools as options, but neither tool compatibility nor a particular model’s performance is guaranteed by the WSL qualification alone.
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Start by identifying the DGX Station you actually have
This guide’s official scope is the current GB300 Station
NVIDIA’s current DGX Station Development Guide and its system overview describe the GB300 generation. That is the platform for which the ARM64 WSL route is documented. Check the model name on the chassis, purchase documentation, management interface, or system information before changing software. “DGX Station” is not a single, interchangeable hardware specification.
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Legacy Stations need a separate support check
Earlier DGX Station models may use different CPUs, GPUs, firmware, driver branches, or operating-system images. The GB300 statements in this article do not establish support for those systems. If your machine is not identified as GB300, use the documentation for that exact generation and do not copy the architecture, driver, or runtime assumptions below.
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Understand what “Windows” means in this setup
The Station’s native environment is Linux
A factory GB300 Station is preinstalled with Ubuntu 24.04 and NVIDIA’s AI Developer Tools. Ubuntu is therefore the native place to manage the machine and run its Linux software stack. A Windows computer can be used as the interactive host, but that does not turn the Station into a native Windows workstation.
WSL is a bounded integration route
NVIDIA’s system-readiness documentation identifies an ARM64 WSL route for a DGX Station GB300. In this arrangement, Windows supplies the user interface and host environment while Linux in WSL supplies the distribution and user-space runtime that local AI software expects. The GPU must be exposed correctly through that path. Treat the qualification as a specific platform-and-configuration statement, not as proof that every Windows edition, WSL distribution, model, or package will work.
What the qualification does not promise
- It does not certify every older DGX Station generation.
- It does not make an arbitrary native Windows installation use the Station GPU.
- It does not guarantee an ARM64 build of every Python package, inference server, quantization library, or model dependency.
- It does not prove that a particular model will fit, run at a useful speed, or use both the GPU and CPU memory as you expect.
NVIDIA also publishes an AI Workbench Windows full-local reference. Use it for the general Windows/WSL concepts it covers, but do not treat that reference as a GB300-specific model compatibility matrix.
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A clear mental model prevents most setup mistakes. The intended path is:
- Windows host: your desktop, terminal, editor, or browser connects to the Station or hosts the WSL session, depending on your deployment.
- ARM64 WSL environment: a Linux user space matched to the GB300 route identified by NVIDIA.
- NVIDIA driver and compute interface: the layer that makes the GPU visible to Linux applications.
- Inference or development runtime: Ollama, vLLM, SGLang, PyTorch, Jupyter, or another verified stack.
- Model files and service endpoint: weights are stored on the Station or an attached volume, and the runtime exposes a local API, notebook kernel, or command-line interface.
If any layer is unsupported or mismatched, the failure may look like a model problem even though the real cause is architecture, driver visibility, or package availability.
Preflight checklist for a GB300 Windows/WSL workflow
Record the platform identity
- Confirm that the machine is a DGX Station GB300.
- Record the installed Ubuntu release and NVIDIA software image rather than assuming they match a tutorial written for another generation.
- Note whether Windows is on the same physical system, a management workstation, or a separate computer. The network and display path differ in each case.
Confirm the WSL architecture and distribution
The documented route is ARM64 WSL. Verify the architecture reported inside the distribution; a distribution running as x86-64 is not equivalent merely because its name contains “Ubuntu.” Keep one dedicated distribution for the AI stack so that driver, Python, and runtime packages are not mixed with unrelated experiments.
Check GPU visibility before installing a model runtime
From the Linux environment, run a diagnostic such as nvidia-smi and confirm that it reports the expected NVIDIA device. This is a visibility check, not a complete compatibility test: a GPU can appear while a required framework, kernel, or model operation still lacks a compatible build. If the command is unavailable or shows no device, stop at this stage and resolve the host/WSL/driver path first.
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Plan storage and data movement
Model weights, tokenizer files, caches, logs, and temporary compilation artifacts can consume far more space than the runtime package. Keep them on storage attached to the Station or on a fast, deliberately mounted volume. Avoid placing a large model cache in a temporary Windows location that is routinely cleaned or disconnected. Decide in advance how Windows clients will reach the inference endpoint and which interface will be allowed to listen.
Choose the model and runtime together
Write down the model family, parameter count, precision or quantization, context length, expected concurrency, and required operators before selecting software. A runtime may support a model format but not its newest architecture, and a model may fit in memory while still exceeding the runtime’s supported context or kernel set.
Choose a local AI tool by the job you need to do
NVIDIA’s guide says developers can use PyTorch, Jupyter, vLLM, SGLang, and Ollama to prototype, fine-tune, and run inference. The tools serve different purposes; there is no universal best choice established by the cited documentation.
| Tool or stack | Best fit | Configuration profile | GB300/WSL decision to make |
|---|---|---|---|
| Ollama | A straightforward local-model workflow and a simple local API | Lower setup friction; fewer low-level controls than a custom server | Verify an ARM64-compatible build, the model format, GPU offload behavior, and whether the desired model is in its supported catalog |
| vLLM | A configurable inference server, batching, and API-oriented development | More control over serving parameters and memory behavior; more dependencies to validate | Check that the selected release and all compiled components support the Station’s ARM64 Linux environment and your model architecture |
| SGLang | Programmable serving and research-oriented inference workflows | Powerful configuration with a larger compatibility surface | Verify the exact framework, CUDA/NVIDIA stack, model, and launch options as one combination |
| PyTorch | Custom inference, fine-tuning, evaluation, and application development | Maximum control; you assemble loading, tokenization, batching, and serving | Use a build that matches the available ARM64 platform and NVIDIA software, then test the model’s operators |
| Jupyter | Interactive notebooks, inspection, experiments, and teaching | Convenient exploration rather than a production serving layer | Confirm that the notebook kernel sees the same GPU and Python environment as the runtime you intend to deploy |
Start with one tool and one small, known-compatible model. Add a second runtime only after the first can load a model, execute a prompt, and report GPU use. Running several overlapping environments makes it harder to tell whether a failure comes from the model, package resolver, or device layer.
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1. Establish the support boundary
Write down “DGX Station GB300,” the Ubuntu image, the Windows host architecture, the WSL distribution architecture, and the NVIDIA software versions. Compare that combination with the current NVIDIA readiness guidance. If any item is unknown, do not proceed by substituting a generic desktop-GPU tutorial.
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- It has a large capacity of memory necessary for working with a vast array of data sets and workloads such as rendering, data science, and simulation
2. Prepare the Windows and WSL side
Enable and update WSL using the procedure appropriate to your Windows release, then create or select the ARM64 Linux distribution required by the GB300 route. Keep the distribution current enough for the documented NVIDIA integration, but do not replace the Station’s Ubuntu image with an unsupported Windows image. The Windows reference from NVIDIA can help explain a full-local WSL arrangement, while the GB300 readiness page defines the platform-specific boundary.
3. Verify Linux identity and GPU access
Inside WSL, check the machine architecture with uname -m and confirm the expected ARM64 result for the documented route. Then run nvidia-smi. Record the reported GPU, driver, and memory information. If either check disagrees with the intended configuration, fix that mismatch before installing Python packages or downloading weights.
4. Create an isolated software environment
Use a dedicated virtual environment or equivalent isolation for each major runtime. Pin the versions you select and keep a record of the package source and architecture. Do not assume that a package resolver will silently choose a correct ARM64 wheel; some projects publish only x86-64 binaries, while others require a source build or a vendor-specific package.
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Choose Ollama for a simple local endpoint, vLLM or SGLang for a configurable server, or PyTorch/Jupyter for application and experiment work. Follow the selected project’s current Linux-on-ARM64 instructions and NVIDIA’s platform requirements. The available documentation does not provide one tested, model-specific Windows command sequence, so treat launch commands from a project as examples that still require validation on your exact GB300/WSL stack.
6. Download and stage a model
Use the model publisher’s stated format and license, and store the files where the Linux runtime can read them reliably. Record the model’s parameter count, precision, quantization, context limit, tokenizer requirements, and any custom code it needs. Begin with a model small enough to leave headroom for the operating system, runtime, KV cache, and concurrent requests.
7. Run a smoke test
Start the runtime with its documented local configuration and send one short prompt. Check four results separately: the process stays alive, the model produces a valid response, GPU memory or utilization changes during generation, and the response latency is plausible for the selected model. A successful response alone does not prove GPU acceleration; a CPU fallback can still answer.
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8. Expose the service deliberately
For a Windows application or browser to use the model, bind the service and forwarding path according to the runtime’s documentation. Prefer loopback access while testing. If another machine must connect, restrict the listening interface and firewall rule, require authentication where supported, and avoid exposing an unauthenticated inference endpoint to an untrusted network.
9. Validate the real workload
Test the prompts, context lengths, concurrency, streaming mode, tool calls, and structured-output features your application will actually use. Measure warm-up time, steady-state generation, memory growth, and failure behavior. Keep these observations tied to the exact model, runtime version, precision, and WSL configuration; they are not transferable performance guarantees.
Interpret GB300 capacity without overpromising
NVIDIA publishes the following capabilities for the current Station. They describe hardware potential, not an independent benchmark or a promise that every runtime will use the resources efficiently.
| Published figure | What it means | Important qualification |
|---|---|---|
| Up to 252 GB HBM3e | GPU high-bandwidth memory available in the stated configuration | Usable capacity depends on firmware, runtime allocation, model format, KV cache, and other workloads |
| Up to 496 GB LPDDR5X | CPU system memory in the stated configuration | CPU memory is not the same as GPU HBM bandwidth; offloading can change speed and compatibility |
| Up to 748 GB coherent system memory | The combined HBM3e and LPDDR5X figure NVIDIA reports | It is a vendor capability claim, not a guarantee that a model can use all 748 GB as one undivided pool |
| Up to 20 PFLOPs of AI compute | NVIDIA’s peak AI-compute specification | Actual throughput varies with precision, kernels, batching, model architecture, and software |
| Models up to 1 trillion parameters | NVIDIA’s stated model scale the system can work with | “Up to” does not mean every trillion-parameter model fits a chosen runtime or delivers acceptable latency |
For planning, estimate weights, runtime overhead, KV cache, and safety margin separately. A parameter count by itself is insufficient: two models with the same count can have different precision, context, architecture, and memory behavior. Treat any model-size estimate as a starting point and confirm it with the runtime’s own memory report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshoot by locating the failing layer
The Station boots Ubuntu, not Windows
That is expected for the current GB300 image. Use Ubuntu for native administration and run the Windows workflow through the qualified host/WSL arrangement. Reinstalling the Station with a generic Windows image would move outside the documented scope.
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WSL starts, but the architecture is wrong
If the Linux environment reports x86-64 instead of the ARM64 route required for GB300 qualification, stop and create or select the correctly matched distribution. Do not try to repair the mismatch by installing random architecture-specific wheels into the existing environment.
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- Achieves up to 3 times better AI performance than previous generations, supports faster FP8 precision data and accelerates the execution of mixed flotation decimal and whole numbers
- It has a large capacity of memory necessary for working with a vast array of data sets and workloads such as rendering, data science, and simulation
nvidia-smi cannot see a device
Check the Windows host integration, the WSL distribution, the NVIDIA driver/runtime path, and the Station’s own software image in that order. A model package cannot fix a missing device interface. Recheck after each change and keep the first configuration that produces a stable GPU report.
The runtime installs but falls back to CPU
Confirm that the runtime selected a GPU-enabled build, that its framework can load the model architecture, and that the process is running inside the same WSL environment where the GPU check succeeded. Inspect runtime logs for unsupported operators or an explicit CPU fallback. Reduce the model or context only after device support is established.
The model runs out of memory
Lower the context length or concurrency, use a supported precision or quantization, unload other models, and leave room for KV-cache growth. Moving some work to CPU memory can change latency substantially and may not be supported by every runtime. Do not infer from the Station’s headline memory figure that an arbitrary model configuration will fit.
A package has no ARM64 build
Check whether the project publishes an ARM64 Linux release, provides a supported source build, or lists an alternative runtime. Building locally can introduce compiler and CUDA compatibility issues; record the exact build and test it with a small model before relying on it.
Windows cannot reach the local endpoint
First prove that the server responds inside Linux. Then inspect the WSL-to-Windows forwarding or bind address, local firewall rules, and the port configured by the runtime. Keep the service on loopback until connectivity works, and do not open a wider network path merely to bypass an unresolved routing problem.
Performance changes after an update
Record the model digest or release, runtime version, driver and framework versions, WSL distribution image, precision, context, and concurrency. Change one layer at a time and rerun the same smoke test. NVIDIA’s “up to” specifications are not a substitute for workload-specific measurement.
Security and operational practices
- Keep model licenses and downloaded weights in an inventory, especially when multiple Windows users can access the Station.
- Run untrusted model code only when the runtime and isolation policy explicitly support it; a model repository can contain custom code or unsafe files.
- Use least-privilege Linux and Windows accounts, protect API keys, and avoid unauthenticated network exposure.
- Separate experimental environments from any service that handles private prompts or business data.
- Back up configuration and pin versions so that a WSL, driver, or runtime update does not silently change model behavior.
What a successful setup looks like
You have a GB300 Station identified by generation, a documented ARM64 WSL arrangement, a Linux session that reports the intended NVIDIA GPU, and one runtime that loads a specifically chosen model without CPU fallback or memory errors. Windows applications can reach the local endpoint through a deliberately configured and secured path, while the Station continues to use its native Ubuntu environment. Anything broader—support for a legacy Station, a generic Windows installation, or every model and runtime combination—requires separate documentation and testing.
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