Why Your Brand-New GPU Still Fails

PyTorch, CUDA, and the Blackwell Blind Spot

You've got a cutting-edge RTX 5060 Ti. CUDA is installed. PyTorch reports that CUDA is available. Everything should work.

Then you run your script and hit this:

RuntimeError: CUDA error: no kernel image is available for execution on the device

PyTorch sees the GPU but can't run code on it. Why?

🤯 The Real Problem

This error means your PyTorch installation includes CUDA support, but not for your specific GPU architecture.

Blackwell GPUs (like the 5060 Ti) use a compute capability called sm_120. The PyTorch binaries you're installing from pip were not built with support for sm_120, even if they were compiled against CUDA 12.6.

No sm_120 in the build = no working CUDA kernels = crash.

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✅ Quick Fix: Force CPU Mode

If you want to get running immediately, you can bypass the GPU.

Look for code like this:

model.to("cuda" if torch.cuda.is_available() else "cpu")

Change it to:

model.to("cpu")

Or hard-code your app's logic to avoid trying to use the GPU at all. In one project, I had to do:

models = {False: KModel().to("cpu").eval()}

You can also force:

CUDA_AVAILABLE = False

This disables GPU execution entirely - slower, but at least it works.

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🛠 Long-Term Fix (Recommended): Install a Custom .whl with GPU Support

Here's how to fix this the right way-by installing a version of PyTorch compiled with support for sm_120 (your GPU's compute architecture).

🔍 Step-by-Step: Finding and Installing the Right .whl

1. Go to the official PyTorch .whl page for CUDA 12.8
   This is the hidden goldmine:
   → https://download.pytorch.org/whl/cu128/torch_stable.html

2. Match your Python version
   For example:

   • Python 3.11 → look for cp311

   • Python 3.10 → look for cp310

3. Download these three files (all must match the same CUDA & Python version):

   • torch-*.whl

   • torchvision-*.whl

   • (optional) torchaudio-*.whl

4. Install them in order using pip
   Replace the paths with where you saved the files:

pip install torch-2.7.0+cu128-cp311-cp311-win_amd64.whl

pip install torchvision-0.22.0+cu128-cp311-cp311-win_amd64.whl

pip install torchaudio-2.7.0+cu128-cp311-cp311-win_amd64.whl

5. Verify CUDA works:

python -c "import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))"

You should see something like:

True

NVIDIA GeForce RTX 5060 Ti

🧠 Why This Works

The standard pip install torch only gives you builds for older architectures (like sm_86 or sm_90). But Blackwell (your 50xx GPU) uses sm_120, which isn't included in the default PyTorch wheels.

Luckily, PyTorch 2.7+ does support sm_120, it's just not distributed via pip yet. That's why downloading the .whl manually from the PyTorch site solves the problem.

📦 Still Not Working?

• You're on the wrong Python version. The .whl must match your Python version exactly.

• Wrong CUDA version. These .whl files are for CUDA 12.8. You must have the CUDA 12.8 runtime (or later) available.

• Using a weird IDE or environment. Try using a fresh venv and run from PowerShell or Terminal.

🧪 Final Tip: Lock the Setup

Once it's working, save your setup in a requirements.txt so you can replicate it later:

pip freeze > requirements.txt

Now you're set. Your RTX 50xx is finally working the way it should.