On an RTX Spark running Windows on Arm, install the NVIDIA driver first, then the Windows-on-Arm CUDA Toolkit (currently 13.4 in NVIDIA’s guide), and finally NVIDIA’s ARM64 PyTorch wheels inside a clean virtual environment. Add one AI application at a time and check NVIDIA’s Spark dependency matrix before pinning libraries such as cuDNN, TensorRT or Triton.
The order matters because RTX Spark uses unified memory shared by the CPU and GPU, while Windows ARM64 packages are not interchangeable with x86_64 or Linux ARM64 packages. The procedure below separates the supported Windows path from DGX Spark-style Linux deployments and shows which local AI tool fits each job.
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Before you install: identify the exact RTX Spark platform
“RTX Spark” covers systems built around the same class of NVIDIA hardware but running different operating systems. This guide’s commands target a Windows ARM64 RTX Spark. NVIDIA’s CUDA documentation identifies Windows on Arm support for RTX Spark devices, and the RTX Spark Windows-on-Arm guide specifies CUDA Toolkit 13.4 for native development and cross-compilation from the Windows x86_64 toolkit.
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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 & 11Do not apply a Linux ARM64 command to Windows, or assume that an x86_64 Python wheel will work natively on ARM64. A DGX Spark-style Linux installation has its own support entries: NVIDIA’s matrix dated April 15, 2026 lists CUDA Toolkit 13.0 for that environment. The version difference is a platform distinction, not an instruction to downgrade a Windows installation.
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Confirm Windows architecture
- Open Settings > System > About and check System type, or open System Information and inspect System Type.
- Confirm that the machine reports an ARM64-based processor. If it reports x64, stop and follow the x64 installation instructions for that machine instead.
- Install the current NVIDIA driver offered for the RTX Spark device and reboot. A driver update should precede CUDA and Python package installation so that later checks describe the intended driver, not an older one.
Install CUDA Toolkit on Windows ARM64
Use NVIDIA’s CUDA documentation and the RTX Spark Windows-on-Arm page as the authoritative installer and release-note references. For the Windows ARM64 path described by NVIDIA in 2026, the guide points to CUDA Toolkit 13.4. The toolkit supplies the compiler and core CUDA libraries needed by native applications; it is separate from the Python packages that provide frameworks such as PyTorch.
Install and reboot
- Download the Windows-on-Arm CUDA Toolkit release selected in NVIDIA’s RTX Spark guide.
- Run the installer with the default components unless a project specifically requires a customized component set.
- Reboot Windows after installation. This avoids diagnosing a shell that has not yet received updated paths or driver state.
Check driver and compiler visibility
Open a new PowerShell window and run:
nvidia-smi
nvcc --versionnvidia-smi should display the NVIDIA driver and device. nvcc --version reports the CUDA compiler visible in the current environment. These commands tell you what Windows can see; they do not prove that every Python framework or AI application is compatible with the installed toolkit.
If either command is missing, reboot once more and check that the NVIDIA installation completed. If the driver appears in nvidia-smi but nvcc is unavailable, the driver is present but the toolkit or its path is not. Repair or reinstall the toolkit rather than copying an x86_64 CUDA directory into an ARM64 installation.
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Create an isolated ARM64 Python environment
Use a native ARM64 Python build recommended for your Windows-on-Arm image. Avoid mixing an emulated x86 Python interpreter with native ARM64 wheels: the interpreter architecture, wheel architecture and dependent libraries must agree.
Make a project environment
- Open PowerShell in a new project directory.
- Create the environment:
py -m venv .venv- Activate it:
. venvu0000Scriptsu0000Activate.ps1In a normal PowerShell command, the path is . venvu0000Scriptsu0000Activate.ps1 with the directory name written as .venv; the escaped representation above keeps the backslashes visible in HTML. If PowerShell blocks script activation under your policy, use the approved execution-policy procedure for your organization rather than disabling security controls globally.
- Upgrade packaging tools inside the active environment:
python -m pip install --upgrade pipKeep separate environments for separate applications. An image-generation stack can require a different pre-release or extension set than an inference server; installing both into one environment makes accidental replacement of torch much more likely.
Install NVIDIA’s ARM64 PyTorch build
NVIDIA’s RTX Spark Windows-on-Arm guide publishes a pre-release wheel command for torch, torchvision and torchaudio. Run it only after activating the intended virtual environment:
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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 --only-binary=:all: option prevents pip from silently switching to a source build, while the NVIDIA extra index supplies the wheel set intended for RTX Spark Windows ARM64. The --pre flag is significant: these are pre-release packages, so availability and dependency resolution can change.
Record the working wheel set
After a successful install, capture the resolved versions:
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python -m pip freeze > requirements-lock.txtCommit that file with the project or otherwise store it with your deployment notes. Pinning the exact set after you confirm it works gives you a reproducible starting point without pretending that future NVIDIA nightlies will remain identical.
Verify CUDA from Python
Create a file named check_torch.py:
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')Run it with:
python check_torch.pyA result of True for torch.cuda.is_available() and an RTX Spark device name indicate that the active interpreter can access CUDA. The check is deliberately small: it separates a framework/driver problem from failures introduced later by a model, extension or user interface.
Use NVIDIA’s compatibility matrix before adding libraries
CUDA itself is only one layer. NVIDIA’s Spark support matrix, last updated April 15, 2026, lists the following entries for a supported Spark software stack:
| Component | Matrix entry | Why it matters |
|---|---|---|
| CUDA Toolkit | 13.0 | Matrix entry for the Spark Linux support set; Windows-on-Arm guidance currently points to 13.4. |
| cuDNN | 9.11 | Neural-network primitives used by frameworks and inference software. |
| TensorRT | 10.14.1 | Optimized inference runtime; applications may require a matching build. |
| TensorRT-LLM | 1.2 | Large-language-model serving and optimization components. |
| Triton | 25.08 | Compiler/runtime layer used by some accelerated kernels and serving stacks. |
| Container Toolkit | 1.17.7 | Container GPU integration where a supported container workflow is used. |
The matrix also notes that CUDA libraries including cuBLAS, cuFFT and cuSPARSE are included with the CUDA Toolkit. Do not install a second copy merely because an application’s readme lists those library names. Check the matrix and the application’s Spark playbook first, then install only the version and platform combination it names.
Choose an AI tool by the job you need done
NVIDIA’s current Spark catalog includes Ollama, llama.cpp, LM Studio, ComfyUI, vLLM, SGLang, Unsloth and TensorRT-LLM workflows. The right first install depends more on the workload than on the size of the GPU.
| Goal | Good starting point | Decision notes |
|---|---|---|
| Simple local LLM chat or a small local API | Ollama | Choose it when its model format and client workflow match your application. |
| Lightweight GGUF inference | llama.cpp | NVIDIA’s PAIR example uses GGUF models with CUDA on RTX Spark Windows ARM64. |
| Desktop model management | LM Studio | Useful when a graphical catalog and desktop workflow are more important than scripting. |
| Image or video diffusion graphs | ComfyUI | NVIDIA lists workflows for FLUX, Wan, HunyuanVideo and Stable Diffusion. |
| High-throughput serving | vLLM, SGLang or TensorRT-LLM | Use the Spark playbooks and matrix to select a supported release before deployment. |
| Fine-tuning | Unsloth or PyTorch | Use NVIDIA’s Unsloth-on-Spark playbook or build directly on the verified PyTorch environment. |
Ollama for the shortest path to local chat
Ollama is the pragmatic first choice when you want a local model endpoint without assembling a serving stack. Install it in the manner documented for Windows ARM64, then select a model whose runtime supports the available GPU backend. Keep its model files outside your Python virtual environment; Ollama is an application/runtime, not a replacement for the PyTorch installation above.
llama.cpp for controllable GGUF inference
llama.cpp is a better fit when you specifically use GGUF files, want command-line control or need a lightweight server. NVIDIA’s PAIR example demonstrates CUDA-enabled llama.cpp with GGUF models on RTX Spark Windows ARM64. Build or install the ARM64 release intended for your platform and confirm CUDA is enabled before measuring performance.
LM Studio for a graphical desktop workflow
LM Studio suits users who prefer downloading, organizing and chatting with models through a desktop interface. Its supported catalog and backend determine which models and quantizations are available. Treat it as an independent application: if it installs or updates its own runtime, do not let that overwrite the project’s verified PyTorch environment.
ComfyUI for diffusion media
ComfyUI represents image and video generation as a graph of nodes. NVIDIA lists Spark workflows covering FLUX, Wan, HunyuanVideo and Stable Diffusion. Start with a known workflow, then add custom nodes one at a time. Custom nodes can impose their own Python and CUDA constraints, so install them in a dedicated environment rather than the environment used for a stable text-generation service.
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vLLM, SGLang and TensorRT-LLM for serving
These tools target server-style throughput, batching and API deployment rather than a single-user desktop chat. Their native dependencies are more sensitive to CUDA, Triton and TensorRT versions. Read the Spark-specific playbook and matrix entry before installing; a generic Linux x86_64 command is not evidence of Windows ARM64 support.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAccount for RTX Spark’s unified memory
NVIDIA reports RTX Spark configurations with up to 128 GB of unified memory and up to 1 petaflop of AI compute in its October 13, 2026 launch article. Unified memory is shared by the CPU and GPU, so the headline capacity is not an amount that a model can consume exclusively.
Reserve capacity for Windows, background applications, the model’s context, CUDA allocations, tokenizer buffers, caches and the runtime itself. A model’s download size is only the starting point. The practical requirement is approximately:
weights + context/KV cache + runtime overhead + operating-system headroomQuantization can reduce weight storage, but a longer context window increases cache use. If a model fails to load, try a smaller quantization or context, close memory-heavy applications, and compare the complete requirement with available unified memory instead of comparing only the file size.
Optional multi-system inference with NVIDIA PAIR
PAIR 1.0.0 supports Windows 11, Linux and macOS 13 or later, with x64 and ARM64 packages. RTX Spark uses the Windows ARM64 package; DGX Spark uses Linux ARM64. PAIR exposes Ollama-compatible and OpenAI-compatible proxy endpoints and routes independent requests according to engine availability, model availability and workload.
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What you need
- A trusted local network.
- PAIR installed on every participating system.
- An available engine: llama.cpp, Ollama or LM Studio.
- At least one model present on a serving system.
The PAIR playbook estimates about 10 minutes for setup, excluding model download time, and was last updated October 6, 2026.
What PAIR cannot do
PAIR distributes independent requests; it does not pool the memory of several computers to load one model that would not fit on any single machine. Each serving system still needs enough unified or local memory for its model weights, requested context and runtime overhead.
When routing fails
- Complete the six-digit setup PIN flow on each device.
- Confirm that every device is on the trusted network and can reach the others.
- Check that the selected engine is running and that its model is downloaded on a serving system.
- Use the exact model identifier expected by the engine; a display name copied from a catalog is not always the identifier used by an API.
Troubleshoot in dependency order
torch.cuda.is_available() returns False
- Run
nvidia-smiand confirm that the driver sees the RTX Spark device. - Check the active interpreter with
python -c "import sys; print(sys.executable)"and make sure it is the project’s.venv. - Inspect the installed package with
python -m pip show torch. - Confirm that the wheel set is for Windows ARM64, not x86_64.
- Reinstall the NVIDIA wheel command inside the activated environment if another package replaced it.
An AI tool downgrades or replaces PyTorch
Do not keep layering fixes onto a contaminated environment. Remove the environment, recreate it, reinstall the NVIDIA wheel set, run the CUDA verification script, and then add the AI tool in a separate environment if its dependencies are not explicitly compatible with the verified build.
A model will not load
Check total weights, context/KV cache and runtime overhead against free unified memory. Lowering context length or choosing a smaller quantization can change the memory requirement substantially. Also close browsers, editors and other GPU-using applications before concluding that the model is unsupported.
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Compare its required CUDA, TensorRT, Triton and Python versions with the Spark matrix and the tool’s Spark playbook. A package that installs on Linux ARM64 or Windows x86_64 may still lack a native Windows ARM64 wheel. Prefer a documented ARM64 wheel or container path over forcing a source build with unknown compatibility.
Quick Recap
A repeatable installation checklist
- Confirm Windows ARM64 in System Information.
- Update the NVIDIA driver and reboot.
- Install the CUDA Toolkit version named by the RTX Spark Windows-on-Arm guide, currently 13.4.
- Verify
nvidia-smiandnvcc --version. - Install a native ARM64 Python build.
- Create and activate a project-specific virtual environment.
- Install the NVIDIA ARM64 pre-release PyTorch wheels from the published extra index.
- Run a minimal PyTorch CUDA check and record the resolved package versions.
- Consult the Spark matrix before adding cuDNN, TensorRT, Triton or serving frameworks.
- Install one AI tool in a compatible environment and account for unified-memory headroom before downloading large models.
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