PyTorch CPU vs CUDA Wheel Choice

PyTorch CPU vs CUDA Wheel Choice | Generated by AI

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Question: How does PyTorch distribute CPU-only vs CUDA wheels, and which one should I install on Windows?

Answer

PyTorch distributes separate wheels because CUDA binaries are massive and optional. The CPU wheel (~200 MB) contains only:

PyTorch runtime code
CPU kernels (BLAS, MKL-DNN)
Python bindings

The CUDA wheel (2-3 GB) bundles:

Everything from CPU wheel
NVIDIA CUDA runtime libraries (cudart, cublas, cunn, etc.)
cuDNN (deep learning primitives)
compiled GPU kernels for your CUDA version

Why? Users running inference on CPU-only systems don’t need 2GB of unused CUDA binaries. Wheels with bundled CUDA also lock you to a specific CUDA version—if you upgrade CUDA later, a bundled wheel won’t match.

Installation on Windows

CPU-only (fastest install, no GPU):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

CUDA 12.4 (most common, requires NVIDIA GPU + CUDA 12.4 installed):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

CUDA 11.8:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

The --index-url flag tells pip to fetch from PyTorch’s wheel repository instead of PyPI (which only hosts CPU wheels by default).

Verify what you got

import torch
print(torch.cuda.is_available())  # True if CUDA wheel installed correctly
print(torch.__version__)
print(torch.version.cuda)  # None if CPU, "12.4" if CUDA 12.4, etc

Key details

You need CUDA runtime installed separately — the wheel bundles cuBLAS/cuDNN but still needs NVIDIA drivers + CUDA toolkit. Check: nvidia-smi should work.
CUDA 12.4 wheel works with CUDA 12.1+ (forward-compatible within major version).
RTX 4070 on your workstation needs CUDA 12.x. Grab the cu124 wheel.
Install once, use everywhere — after pip install, you just import torch and pick device: torch.device('cuda') vs torch.device('cpu').

If you’re doing serious training on your 4070, don’t skip the CUDA wheel. CPU training on a GPU-capable machine is painful.

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