FAQ

How do I run TiRex-2 on CUDA?

With device="cuda", TiRex-2 builds its fused sLSTM kernel (FlashRNN) with nvcc on the first forecast. In order to run TiRex-2 on CUDA you need:

1. A CUDA Toolkit installed and discoverable — nvcc must be on PATH or reachable via CUDA_HOME.
2. A CUDA Toolkit whose major version matches your PyTorch build — any 12.x toolkit for a cu12x torch wheel, any 13.x toolkit for a cu13x one. Check with python -c "import torch; print(torch.version.cuda)".
3. A CUDA Toolkit no newer than your driver supports.

Which NVIDIA GPU architectures does TiRex-2 support?

• TiRex-2 runs on NVIDIA GPUs with compute capability 8.0 (Ampere) or newer.
• Older cards — Turing (7.5), Volta (7.0) and earlier — cannot run device="cuda"; use device="cpu" instead.

Why does TiRex-2 fail with where cl on Windows?

PyTorch may compile model components at runtime, so the Python process needs access to the MSVC C++ compiler, even when using device="cpu".

Install Visual Studio Build Tools with Desktop development with C++, then run TiRex-2 from an x64 Native Tools Command Prompt for Visual Studio. Confirm the compiler is available before starting your script:

where cl
python your_script.py

Launch VS Code or Jupyter from the same prompt so it inherits the compiler environment. See #15 and #17 for related reports.