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Use NVIDIA’s Windows-on-Arm instructions, install the toolkit version that matches your RTX Spark image, then add PyTorch inside a fresh ARM64 Python virtual environment. For current RTX Spark Windows systems, NVIDIA’s guide points to CUDA Toolkit 13.4 and publishes an ARM64 PyTorch wheel command. Verify the driver and CUDA installation before adding applications such as Ollama, llama.cpp, LM Studio, ComfyUI, vLLM, SGLang, TensorRT-LLM or Unsloth.
This order matters because RTX Spark uses unified memory shared by the CPU and GPU, and because a normal x86_64 Python wheel or an incompatible CUDA library can leave an otherwise healthy installation unable to see the GPU.
What RTX Spark supports
NVIDIA’s October 13, 2026 launch information describes RTX Spark systems with up to 128 GB of unified memory and up to 1 petaflop of AI compute. The same announcement says RTX Spark laptops become available on October 16, 2026, and that PyTorch, Python, CUDA and CUDA libraries are available for the platform. Those figures describe the platform maximum, not the memory or compute available in every configuration.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThis guide is for an RTX Spark running Windows on Arm (ARM64). CUDA documentation identifies Windows on Arm as a supported environment for RTX Spark. NVIDIA’s Windows-on-Arm guide says CUDA Toolkit 13.4 enables native Windows-on-Arm development and cross-compilation from the Windows x86_64 toolkit. A DGX Spark-style Linux ARM64 machine is a different target: NVIDIA’s support matrix lists CUDA Toolkit 13.0 for that environment, with the matrix last updated April 15, 2026. Do not install a Linux package or an x86_64 wheel on Windows ARM64 just because the CUDA version number looks similar.
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Before you install anything
Confirm that Windows is ARM64
- Open Settings > System > About.
- Check System type. The RTX Spark Windows installation should report an ARM64-based processor or ARM64 operating system.
- Record the Windows edition and build so you can match NVIDIA’s current Windows-on-Arm instructions.
If the system reports x64, stop and use the x64 instructions and packages intended for that computer. Architecture is a package-compatibility requirement, not an optional performance setting.
Update the graphics driver first
Install the current NVIDIA driver offered for the RTX Spark device, then restart Windows. A toolkit can be installed while an old driver is present, but driver visibility is the first dependency to eliminate when a framework later reports that CUDA is unavailable.
Keep separate environments
Plan one Python virtual environment per project. A diffusion interface, a language-model server and a fine-tuning notebook can require different versions of PyTorch or auxiliary libraries. Installing all of them into the global interpreter makes it easy for a later package installation to replace a working ARM64 build.
Step 1: install the CUDA Toolkit for Windows on Arm
Start with NVIDIA’s CUDA documentation and the RTX Spark Windows-on-Arm guide. For the current RTX Spark guide, select CUDA Toolkit 13.4 for native Windows-on-Arm development. Follow the installer choices supplied for your Windows image rather than copying a command intended for Linux or Windows x86_64.
- Download the Windows-on-Arm toolkit through NVIDIA’s CUDA documentation/download path.
- Run the installer with the options recommended by the RTX Spark guide.
- Allow the installer to finish, then restart Windows if requested.
- Open PowerShell and inspect the installed driver and compiler:
nvidia-smi
nvcc --versionnvidia-smi reports whether the driver can see the GPU. nvcc --version reports the CUDA compiler available on the command path. They identify what is installed; they do not by themselves prove that a particular Python wheel or AI application is compatible.
Do not mix the Windows and Linux Spark stacks
The April 15, 2026 Spark support matrix lists CUDA Toolkit 13.0 for DGX Spark Linux ARM64. That entry is useful when you administer a Linux Spark system, but it is not a reason to replace the Windows-on-Arm toolkit selected by the RTX Spark guide. Windows ARM64, Linux ARM64 and x86_64 each need packages built for that operating system and architecture.
Step 2: create a clean ARM64 Python environment
Install the native ARM64 Python build recommended for your Windows-on-Arm environment. Then open PowerShell in a new project directory and create an isolated environment:
py -m venv .venv
..venvScriptsActivate.ps1
python -m pip install --upgrade pipAfter activation, python and pip refer to the environment in .venv. You can confirm that with:
python -c "import sys; print(sys.executable); print(sys.platform)"PowerShell may block the activation script under a restrictive execution policy. If activation is refused, use the execution-policy procedure approved by your organization, or invoke the environment’s Python directly while you resolve the policy. Do not work around a corporate policy by downloading an unrelated interpreter.
Step 3: install the NVIDIA PyTorch wheels
NVIDIA’s RTX Spark Windows-on-Arm guide publishes this command for its PyTorch build:
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python -m pip install --pre --upgrade --only-binary=:all: --extra-index-url 'https://pypi.nvidia.com/nvtorch_oot_nightly' torch torchvision torchaudioThe --pre option is intentional: the published ARM64 wheels are pre-release builds. --only-binary=:all: prevents pip from silently falling back to a source build, and the extra index directs pip to NVIDIA’s wheel repository. Run the command only after activating the intended virtual environment.
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Record the wheel set you actually use
Once installation succeeds, capture the resolved versions:
python -m pip show torch torchvision torchaudio
python -m pip freeze > requirements.txtBecause pre-release wheels can change, keep the resulting requirements file with the project. Recreate the environment and reinstall that known set if a later tool changes or downgrades PyTorch. Do not replace these packages with a generic x86_64 CUDA wheel unless NVIDIA explicitly documents ARM64 support for that wheel.
Verify CUDA from Python
Save the following as check_cuda.py and run it in the same activated environment:
import torch
print(torch.__version__)
print(torch.cuda.is_available())
print(torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CUDA unavailable')A successful result prints the installed torch version, True, and the detected GPU name. A False result means you should troubleshoot the driver, environment and wheel architecture before installing another AI application.
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Step 4: choose an AI tool by the job
RTX Spark can run several local AI workflows. The best first tool depends on whether you want a simple chat endpoint, a desktop model manager, a diffusion graph, or a production-style server.
| Goal | Good starting point | What to verify |
|---|---|---|
| Simple local language-model chat or API | Ollama | Confirm that the model format and client workflow fit your application and ARM64 installation. |
| Lightweight GGUF inference | llama.cpp | NVIDIA’s PAIR example uses GGUF models with CUDA support on RTX Spark Windows ARM64. |
| Desktop model catalog and GUI | LM Studio | Use the models and runtime versions supported by its current Windows ARM64 package. |
| Image or video diffusion graphs | ComfyUI | NVIDIA lists workflows for FLUX, Wan, HunyuanVideo and Stable Diffusion; check each workflow’s model and extension requirements. |
| High-throughput model serving | vLLM, SGLang or TensorRT-LLM | Follow the Spark playbook and support matrix for the matching versions before installing CUDA extensions. |
| Fine-tuning and experimentation | PyTorch or Unsloth | Keep the NVIDIA PyTorch wheel set isolated from other applications and verify the supported training workflow. |
Ollama for the shortest path to a local endpoint
Ollama is a practical starting point when you want a local model process and an API without assembling a complete serving stack. Choose a model that fits unified memory, then test a small request before loading a larger checkpoint. The application’s model runtime must still support the installed driver and GPU; installing the application does not guarantee that every model uses CUDA.
llama.cpp for GGUF control and portability
llama.cpp is suited to GGUF files and exposes lower-level controls over GPU offload, context and batching. NVIDIA’s PAIR example specifically demonstrates GGUF inference with CUDA on RTX Spark Windows ARM64. Keep the model file, context setting and offload configuration documented so that a result that fits on one Spark system can be reproduced on another.
LM Studio for a graphical desktop workflow
LM Studio is useful when you prefer a model catalog, downloads and chat interface rather than a command-line workflow. Verify that the selected model and runtime are available for the current Windows ARM64 release. A graphical interface does not remove the need to account for context memory and runtime overhead.
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ComfyUI represents image and video generation as connected graphs. Start with a workflow known to support your model family, install its required nodes in the same project environment where possible, and test a modest resolution before increasing batch size or frame count. Diffusion models can consume substantial unified memory through weights, latent buffers, video frames and extensions even when the downloaded checkpoint appears small.
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vLLM, SGLang and TensorRT-LLM for serving
These tools target service workloads rather than a single-user desktop chat. Select versions from NVIDIA’s Spark support matrix and the relevant playbook before installing them. Serving frameworks commonly add compiled CUDA components, so install them only after the base driver, toolkit and PyTorch checks pass.
Check the Spark dependency matrix before adding CUDA libraries
NVIDIA’s Spark matrix, updated April 15, 2026, lists the following entries:
| Component | Matrix entry | Qualification |
|---|---|---|
| CUDA Toolkit | 13.0 | Supported entry for the DGX Spark Linux ARM64 matrix; the RTX Spark Windows-on-Arm guide currently points to 13.4. |
| cuDNN | 9.11 | Use only with the CUDA and framework combination documented for your target system. |
| TensorRT | 10.14.1 | Check the matrix and TensorRT-LLM playbook before installing plugins or engines. |
| TensorRT-LLM | 1.2 | Version listed by the dated Spark matrix; serving requirements can change. |
| Triton | 25.08 | Use the matrix-supported build rather than assuming the newest package is compatible. |
| Container Toolkit | 1.17.7 | Marked supported in the matrix. |
| CUDA libraries such as cuBLAS, cuFFT and cuSPARSE | Included with the CUDA Toolkit | Do not install duplicate copies until a tool’s documentation requires them. |
The matrix is a compatibility reference, not a universal command list. Recheck it immediately before reproducing an installation because minimum versions and Spark support can change. Installing a standalone cuDNN, Triton or TensorRT package first can pull a different CUDA runtime and break the PyTorch environment.
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Plan memory correctly on unified memory
RTX Spark’s unified memory is shared by the CPU and GPU. The advertised maximum of up to 128 GB is not all available for model weights. Windows, background applications, the model’s context window, tokenizer, CUDA allocations, temporary buffers and the application itself all draw from the same pool.
Estimate a workload using the total of:
- Model weights after loading in the selected precision or quantization.
- Context or sequence-cache memory, which grows with prompt length and batch size.
- Runtime workspaces, CUDA allocations and framework overhead.
- Application buffers, latent tensors, video frames or training activations.
- Operating-system and desktop headroom.
A model’s download size is only a starting point. A compressed or quantized file can expand in memory, and a long context can add more usage than expected. If a model barely fits at startup, leave less context, reduce batch size, close other applications or choose a smaller model before attempting production use.
Optional multi-system inference with NVIDIA PAIR
PAIR 1.0.0 supports Windows 11, Linux and macOS 13 or later and provides x64 and ARM64 packages. RTX Spark uses the Windows ARM64 package; DGX Spark uses Linux ARM64. PAIR presents Ollama-compatible and OpenAI-compatible proxy endpoints and routes independent requests according to engine availability, model availability and workload.
What you need
- A trusted local network.
- PAIR installed on every participating system.
- An engine such as llama.cpp, Ollama or LM Studio.
- At least one model installed on a serving system.
- The six-digit setup PIN used by the pairing flow.
The PAIR playbook estimates about 10 minutes for setup, excluding model-download time, and was last updated October 6, 2026.
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PAIR distributes independent requests; it does not pool the memory of several computers to fit one model. Each serving machine must independently hold its model weights, requested context and runtime overhead. A model that cannot load on one Spark system will not become loadable merely by adding a second system to the PAIR group.
Troubleshoot in dependency order
torch.cuda.is_available() returns False
- Run
nvidia-smiand confirm that the driver sees the GPU. - Check that the activated
pythonis the interpreter where torch was installed withpython -m pip show torch. - Confirm that the installed wheel is built for Windows ARM64, not x86_64.
- Compare the toolkit and driver against the RTX Spark Windows-on-Arm documentation.
- Recreate the virtual environment and reinstall NVIDIA’s published wheel command if the environment contains mixed packages.
An AI tool changes or downgrades PyTorch
Stop adding packages to the damaged environment. Create a new virtual environment, reinstall the NVIDIA ARM64 torch, torchvision and torchaudio wheels, verify CUDA, and then install the application. Keep a requirements file for each working project so a tool update can be rolled back without guessing the original versions.
A model refuses to load or crashes during generation
Compare weights, context, runtime allocations and operating-system use with the available unified memory. Reduce context or batch size, close memory-heavy applications, lower image or video dimensions, or select a smaller or more heavily quantized model. Do not use download size as the fit test.
PAIR cannot route a request
- Repeat the six-digit setup PIN pairing flow on the participating systems.
- Confirm that all systems are on the trusted local network and can reach one another.
- Check that an engine is running and that the requested model exists on at least one serving machine.
- Compare the model identifier in the request with the exact identifier exposed by the engine.
- Verify that the chosen engine and model are available on the target architecture.
Installation fails with an architecture or binary error
Inspect the package metadata and active interpreter, then remove the incompatible package from the environment. Do not mix ARM64 and x86_64 wheels to bypass the error. If a project has no native ARM64 dependency, consult its documented Windows-on-Arm support status or run it on a separately supported system.
Quick Recap
A repeatable installation order
- Confirm Windows ARM64 in Settings > System > About.
- Install and reboot after the current RTX Spark NVIDIA driver.
- Install the toolkit selected by the RTX Spark Windows-on-Arm guide, currently CUDA Toolkit 13.4.
- Check
nvidia-smiandnvcc --version. - Install native ARM64 Python and create a project-specific virtual environment.
- Run NVIDIA’s published pre-release PyTorch wheel command.
- Run the Python CUDA verification script and save the resolved package versions.
- Install one AI tool suited to the immediate job, not an entire stack at once.
- Consult the Spark matrix before adding cuDNN, TensorRT, Triton or other CUDA components.
- Load a modest model or workflow first, leaving headroom for unified-memory use.
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