Python GPU Playground — Run PyTorch & CuPy Online on Real NVIDIA GPUs
Press "Run" to execute on a real GPU.
Frequently Asked Questions — Python GPU Playground
Can I run PyTorch online for free?
Yes. This playground lets you run PyTorch code in your browser on a real NVIDIA T4 GPU for free. No installation, no account required to try. Sign in to get 30 free credits per month.
What Python GPU libraries are available?
PyTorch (2.5 with CUDA 12) and CuPy are pre-installed. You can import torch, torchvision, or cupy and run GPU-accelerated code instantly.
Can I run CuPy online?
Yes. CuPy is a NumPy-compatible GPU array library pre-installed on the server. Import cupy and run GPU-accelerated numerical operations without any setup.
What GPU runs my Python code?
Your code runs on an NVIDIA T4 GPU with CUDA 12. PyTorch will report cuda:0 as available with 16 GB of GPU memory.
Can I use this to prototype neural networks?
Yes. Define and run PyTorch models, test custom layers, benchmark operations, or prototype training loops — all on real GPU hardware in your browser.
Is there a CUDA C++ playground too?
Yes. Visit the CUDA playground to write and run CUDA C++ kernels with nvcc on the same NVIDIA T4 GPUs.
Do I need to install Python or CUDA?
No. All execution is serverside. Open the playground, write Python code, click Run.
How is output streamed?
Output streams in real time as your program runs. Print statements appear as they execute, so you can see progress for long-running operations.