Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteiTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
Yes, but Windows is not the DGX Station GB300’s native operating system. NVIDIA ships the current Station with Ubuntu 24.04 and NVIDIA AI Developer Tools. To work from Windows, use a Windows host with a supported Windows Subsystem for Linux (WSL) configuration, and confirm that your exact Station generation is covered. NVIDIA’s published WSL qualification is specifically for the ARM64 route on Station GB300; it is not blanket support for every older DGX Station or every Windows installation.
The practical path is: identify the hardware, validate the Windows/WSL and GPU combination, choose a runtime such as Ollama, vLLM, SGLang or PyTorch, verify that the selected model supports that runtime and architecture, then test inference before building a larger workflow.
What “running AI models in Windows” means on a DGX Station
A DGX Station is a Linux-first workstation. NVIDIA’s current GB300 documentation says the machine is preinstalled with Ubuntu 24.04 and NVIDIA AI Developer Tools, rather than Windows. See NVIDIA’s DGX Station Development Guide introduction and system overview.
PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTherefore, “from Windows” normally describes a Windows computer or Windows-managed environment reaching a Linux user space through WSL. It does not mean that the Station has a supported native Windows image with identical driver, firmware and accelerator behavior. The distinction matters because GPU visibility, processor architecture, Linux libraries and model runtimes all have to line up.
#1 Best Overall
- All-in-One Connectivity: Build a tidy workstation with 17 ports: 3×USB-A 10Gbps, 3×USB-C 10Gbps (2 ports share 60W charging), SD/TF 4.0, 3×3.5mm audio, 2.5G Ethernet, DP1.4, and 2×Thunderbolt 5 downstream ports
- 120Gbps Thunderbolt 5 Speed & M.2 High-Speed Storage: Intel-certified Thunderbolt 5 provides up to 120Gbps bandwidth. Built-in NVMe PCIe Gen4x4 M.2 slot supports 2230–2280 SSDs up to 8TB for fast external storage. Note: Compatible with M.2 NVMe SSDs with heatsink (height≤12 mm; width≤24 mm), Not compatible with SSDs wider than 24 mm, including Seagate FireCuda 530 with Heatsink, Samsung 9100 PRO with Heatsink, WD_BLACK SN850X Premium Model with OEM Heatsink, and Lexar NM1090 Active Fan Version
- Multi-Monitor 8K/6K Output: Supports triple independent displays on Windows (2×TBT5+DP): dual or single 8K@60Hz. Mac supports dual displays. Native compatible with latest Windows and MacBooks for expanded workspace. Note: The DP port does not support DP++. To connect an HDMI monitor, please use an active DP-to-HDMI adapter or cable; passive adapters and cables will not work
- 240W Total System Power & High-Speed Networking (required) Equipped with up to 240W total power and a built-in 2.5GbE network port for fast and reliable wired connections, with lower latency and a more stable connection
- AI Cooling & Aluminum Chassis: AI smart cooling with 60mm ultra-thin fan ensures stable performance under heavy load. Premium aluminum unibody offers great heat dissipation, scratch resistance, and a clean matte finish, paired with a reliable security lock. Note: It is normal for the cooling fan to make noise when it is running and the noise is below 30 dB (data from UGREEN lab)
The official Windows-related qualification covered here is narrow: NVIDIA identifies an ARM64 WSL route for a Station GB300 in its NemoClaw system-readiness documentation. That statement should not be extended to legacy DGX Station generations. If your machine is not a GB300, obtain the documentation for that exact model before changing the operating system or planning a WSL deployment.
First, identify your DGX Station generation
Current GB300 Station
This article’s documented workflow applies to the current DGX Station GB300. NVIDIA describes it as a system with Ubuntu 24.04, integrated NVIDIA development tools and a unified memory design intended for local prototyping, fine-tuning and inference.
Legacy DGX Station systems
Earlier DGX Station models may use different processors, GPUs, firmware, driver branches and supported operating systems. A WSL result that applies to GB300 does not establish support for those systems. Record the exact model name, GPU configuration, host processor architecture and installed software release before following any guide.
Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Information to record
- Exact Station model and generation.
- Whether the machine itself runs Ubuntu or whether Windows is a separate host used to access it.
- Processor architecture reported by the Linux environment, especially whether the WSL route is ARM64.
- GPU model, driver version and the CUDA-compatible software stack supplied for that system.
- The intended model, quantization or precision, context length and runtime.
Do not proceed on the assumption that a product name alone proves compatibility. The model, runtime and platform must be checked as a set.
Native Ubuntu versus a Windows-and-WSL workflow
| Question | Native Station environment | Windows/WSL route |
|---|---|---|
| Operating system | Ubuntu 24.04 is the documented preinstalled environment for GB300. | Windows is the host-facing environment; Linux tools run inside a supported WSL distribution. |
| GPU access | Uses the Station’s native NVIDIA software stack. | Requires Windows, WSL, the NVIDIA driver path and the Linux runtime to expose the GPU correctly. |
| Architecture | Matches the GB300 platform supplied by NVIDIA. | NVIDIA’s cited qualification is for ARM64 WSL on Station GB300; do not assume x86-64 or an older Station is covered. |
| Best fit | Lowest integration risk for development and inference on the Station itself. | Useful when Windows tooling, administration or a Windows-based desktop workflow is required. |
| Evidence available | NVIDIA documents the Station’s software and hardware environment. | NVIDIA documents a bounded ARM64 WSL readiness path, not a universal model-by-model Windows recipe. |
If you control the Station and do not specifically need Windows, native Ubuntu avoids an additional compatibility boundary. Choose WSL when Windows integration is a real requirement, not merely because a model has a Windows download.
Hardware capacity: what the published figures do—and do not—promise
NVIDIA publishes large capacity figures for the GB300 Station, but they are capability claims rather than a performance guarantee for a particular model or runtime.
| Published capability | How to interpret it |
|---|---|
| Up to 748 GB coherent system memory | NVIDIA breaks this into up to 252 GB of GPU HBM3e and 496 GB of CPU LPDDR5X. The “up to” qualifier describes the published system capability, not memory available to every process. |
| Up to 20 PFLOPs of AI compute | A vendor specification. It is not an independently measured result for your model, precision, batch size or WSL configuration. |
| Models up to 1 trillion parameters | NVIDIA says the system can work with models at that scale. It does not mean every trillion-parameter model fits a chosen runtime, context length, quantization or Windows/WSL setup. |
Memory requirements depend on parameter precision, quantization, context length, key-value cache, batch size, framework overhead and whether weights are split between HBM and system memory. Treat the published figures as an upper-bound planning reference. Measure the actual model load and inference behavior in your selected runtime.
Validate the Windows and WSL path before installing a model
NVIDIA’s sources do not provide one independently tested, end-to-end Windows command sequence for a named model on a DGX Station. Use the following as a validation workflow, then follow the current instructions for your exact Station, Windows release, WSL distribution and runtime.
Rank #2
- VD8465 Japanese Authorized Distributor Product
- The speed of FP32 calculation is twice as fast as previous generations, which greatly improves the complex 3D processing and graphics simulation workflow
- Up to 2X the throughput compared to previous generations and significantly faster workloads such as video content rendering, architectural design assessments, and virtual prototypes of product design
- Achieve more than twice the previous generation AI performance improvement, support faster FP8 precision data and accelerate 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
- Confirm the Station generation. Verify that the machine is a GB300 before relying on the ARM64 WSL qualification. For a legacy system, stop and obtain model-specific documentation.
- Confirm the host arrangement. Decide whether Windows is installed on the Station, whether Windows is a separate management computer, or whether the model will run on the Station while Windows only provides a client interface. These are different architectures and have different GPU-access requirements.
- Confirm WSL architecture. The Linux distribution used for the documented Station route must match the ARM64 qualification. An x86-64 distribution or emulation layer is not automatically equivalent.
- Confirm GPU visibility inside Linux. Before downloading model weights, verify that the WSL environment can see the NVIDIA GPU and report the expected device. If the GPU is absent, a model runtime cannot repair the underlying integration.
- Confirm driver and runtime alignment. Check that the Windows-side NVIDIA driver, WSL integration and Linux user-space libraries are from a compatible release family. Do not mix packages copied from an unrelated workstation image.
- Confirm storage and memory headroom. Reserve space for model weights, caches, container layers and logs. Leave capacity for the runtime’s temporary allocations; a model that fits its weight files can still fail during context creation.
- Choose one runtime first. Start with a single supported tool and a small model. Establish a working baseline before adding orchestration, quantization changes or multiple servers.
- Run a short smoke test. Load the model, submit a short prompt and observe GPU utilization, memory consumption, token generation and error logs. Record the exact model revision, runtime version and settings.
NVIDIA’s AI Workbench Windows full-local install reference can be useful for understanding a Windows local-development workflow, but it is not a substitute for checking DGX Station GB300 and ARM64 WSL readiness.
Choose a local AI tool
NVIDIA names PyTorch, Jupyter, vLLM, SGLang and Ollama as common tools for prototyping, fine-tuning and inference. The choice is about workflow, not a universal ranking. A model must be supported by the selected tool on the specific ARM64/Linux and WSL combination.
| Tool | Use it when you need | Trade-off to check |
|---|---|---|
| Ollama | A relatively simple local-model workflow with a model-serving experience. | Convenience does not guarantee that every model, quantization or ARM64 GPU path is supported. Verify the model and accelerator backend. |
| vLLM | A configurable inference server, API endpoint, batching or production-style serving controls. | More configuration and dependency checks. Confirm support for the chosen model, GPU architecture and WSL environment. |
| SGLang | Programmable serving and structured generation workflows where its supported model set and features fit. | Compatibility is model- and version-dependent; validate before committing to a larger deployment. |
| PyTorch | Model development, custom inference, experimentation or fine-tuning. | Provides control rather than a turnkey server. You are responsible for packages, device placement, memory behavior and application code. |
| Jupyter | Interactive notebooks for inspecting models, testing prompts and iterating on code. | A notebook is an interface, not an inference engine by itself; it still depends on a compatible framework and GPU stack. |
“Developers can seamlessly hit the ground running out-of-the-box using common tools and libraries such as PyTorch, Jupyter, vLLM, SG Lang and Ollama to prototype, fine-tune, and run inference.” — NVIDIA, DGX Station Development Guide
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.
Use Ollama when the priority is getting one local model responding with minimal application code. Use vLLM or SGLang when you need a controllable server and are prepared to validate more dependencies. Use PyTorch and Jupyter when the work is experimentation or fine-tuning rather than simply exposing a chat endpoint.
Prepare and validate a model
Start with a known-compatible model
Select a model whose architecture, tokenizer, precision and runtime support are documented for your chosen tool. “The Station can work with models up to 1 trillion parameters” is not a compatibility list. Check whether the runtime supports the model family, whether an ARM64 build or compatible package exists, and whether the model’s license permits your intended use.
Plan memory before loading
- Estimate weight memory from parameter count and precision or quantization.
- Add space for the context window and key-value cache.
- Account for runtime, tokenizer, communication and framework overhead.
- Leave headroom for the operating system, WSL services and other applications.
- Decide whether the runtime will place weights in HBM, system memory or a combination.
A model may load successfully at a short context length and fail when a long document or larger batch is submitted. Test the largest context and concurrency level that your application actually needs.
Capture a reproducible baseline
Write down the model identifier, revision, quantization, context length, batch or concurrency setting, runtime version, driver version and WSL distribution. Also record whether the first request includes model-loading time. Without these details, a later speed or memory comparison is not meaningful.
Recommended Free Tools
Run the first inference from Windows
Once GPU visibility and model compatibility are confirmed, keep the first run deliberately small.
Rank #3
- NVIDIA Quadro Sync II1 compatibility
- 3D stereo support with stereo connector
- NVIDIA GPUDirect for Video support
- NVIDIA GPUDirect Remote Direct Memory Access (RDMA) support
- NVIDIA RTX ExperienceTM
- Launch the supported WSL distribution. Work inside the Linux environment that passed the architecture and GPU checks.
- Activate the runtime environment. Use the installation method and package versions documented for the selected tool and Station software stack. Avoid mixing host Windows Python packages with Linux packages used by the model.
- Start the model service or notebook. Bind it only as broadly as necessary. For a local-only test, prefer an interface reachable from the Windows machine without exposing the service to an untrusted network.
- Load a small, known-compatible model. Observe startup logs for unsupported operators, missing libraries, architecture mismatches and out-of-memory warnings.
- Send a short prompt. Confirm that output is generated and that the GPU, rather than an unintended CPU fallback, is doing the work.
- Repeat with your real prompt shape. Increase context, batch size or concurrency one variable at a time while watching memory and latency.
- Save the working configuration. Keep the model revision, runtime settings and logs with the project so that a Windows update, WSL change or driver update can be diagnosed later.
Because there is no single tested command recipe covering every GB300 Windows arrangement and model, do not copy a generic installation sequence and assume success. Treat the first working run as a compatibility baseline that you can expand carefully.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshoot by symptom
WSL starts, but no GPU is visible
- Recheck that the machine is a GB300 and that you are following the ARM64 WSL scope rather than applying it to an older Station.
- Verify that the Windows NVIDIA driver, WSL integration and Linux user-space components are aligned.
- Confirm that the process is running inside the intended WSL distribution, not a separate environment without GPU access.
- Resolve GPU visibility before debugging the model or changing quantization.
The runtime reports an architecture or binary mismatch
- Check the distribution architecture and every native dependency.
- Do not assume an x86-64 wheel, container or plugin will run natively on ARM64.
- Use a release explicitly supporting the platform, or select another runtime whose model backend does.
The model runs out of memory
- Reduce context length, batch size or simultaneous requests.
- Use a supported lower-precision or quantized variant.
- Choose a smaller model for the initial test.
- Check whether the runtime is duplicating weights or allocating a large key-value cache.
- Remember that total published coherent memory does not equal unrestricted memory for one process.
The model loads but falls back to the CPU
- Inspect startup logs for unsupported GPU operators or an unavailable accelerator backend.
- Confirm that the model architecture is supported by the chosen runtime on this platform.
- Do not judge performance until you have established where the weights and compute are placed.
Output is slow or inconsistent
- Separate one-time model-loading latency from steady-state token generation.
- Keep prompt length, context, batch size and concurrency fixed while comparing runs.
- Check whether Windows background activity, WSL resource limits or memory pressure is affecting the process.
- Report the exact configuration rather than quoting the Station’s maximum PFLOPs as an expected application speed.
A Windows update or driver change breaks the setup
Recheck GPU visibility first, then compare the driver, WSL distribution, runtime and model versions with the saved baseline. Roll back only through your organization’s approved change process; do not replace the Station’s supported software stack with packages from an unrelated system.
Security and operational considerations
Keep local endpoints local unless you need remote access
A model server reachable from Windows can become reachable from other network interfaces depending on its bind address and firewall rules. For a personal test, restrict access to the local machine. If other users need access, define authentication, authorization, network segmentation and logging before exposing an endpoint.
Protect model and prompt data
Local inference keeps prompts and model traffic within your environment only if the application, plugins and monitoring tools do not send data elsewhere. Review the runtime’s telemetry, extension and update behavior, and apply your organization’s retention rules to prompts, outputs and logs.
Plan for updates
Windows, WSL, NVIDIA drivers, Ubuntu packages and model runtimes can change independently. Update one layer at a time, keep a known-good environment, and rerun the short smoke test after each change. A working GB300 baseline is more valuable than an unrecorded collection of package versions.
When native Ubuntu is the better answer
Use the Station’s native Ubuntu environment when you need the least ambiguous support path, maximum access to NVIDIA’s preinstalled developer tools, or a stable Linux server for repeated inference and fine-tuning. Windows/WSL is justified when Windows administration, desktop integration or a Windows-only workflow is a firm requirement and the GB300 ARM64 qualification has been confirmed.
For teams, document both paths separately. A native Ubuntu deployment and a Windows/WSL client workflow can coexist, but they should not be treated as interchangeable installations. The runtime, model files, network exposure and upgrade process belong to the environment where inference actually runs.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
A practical decision checklist
- Generation: Is the machine definitely a DGX Station GB300?
- Architecture: Does the WSL environment match the documented ARM64 route?
- GPU: Is the NVIDIA GPU visible inside WSL before model installation?
- Runtime: Are you choosing a model server, a development framework or an interactive notebook?
- Model: Is the exact model architecture and quantization supported by that runtime?
- Capacity: Have you budgeted for weights, context cache, framework overhead and concurrent requests?
- Validation: Have you measured a short prompt, real context length and expected concurrency?
- Operations: Are endpoint exposure, data handling, version pinning and recovery documented?
The reliable answer to “Can I use WSL with my DGX Station?” is therefore conditional: NVIDIA documents an ARM64 WSL path for the current GB300, but support is not established here for every older Station, every Windows installation or every model/runtime combination. Validate those three layers—hardware, platform and software—before scaling beyond a small local inference test.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
