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Host requirements

Every env pins its own PyTorch (or TensorFlow) and CUDA build, so what your machine needs depends on which models you install.

In short

  • Linux with an NVIDIA GPU. Installation counts as done only after the model computes energy and forces on the GPU.
  • conda or mamba on PATH.
  • An NVIDIA driver new enough for the env's CUDA build — see the table. Check yours with nvidia-smi (the CUDA version at the top right).
  • Disk: each env takes several GB, plus the model weights.
  • Host RAM: 16 GB is enough for every model except UMA-m-1p1-*, which needs 32 GB or more.

Per env

Env Python Framework CUDA Driver Notes
allegro 3.11 torch 2.8.0 12.8 570+ compiled for your GPU at install (CuEquivariance)
alphanet 3.11 torch 2.1.2 12.1 525+
chgnet 3.11 torch 2.7.1 12.6 525+
deepmd 3.11 torch 2.8.0 12.8 570+
dpa4 3.11 torch 2.11.0 13.0 580+
eqnorm 3.11 torch 2.6.0 11.8 450+
equflash 3.12 torch 2.9.1 12.6 525+
equiformer_v3 3.11 torch 2.7.1 12.8 570+
fairchemv1 3.11 torch 2.4.1 12.1 525+ gated weights (eSEN)
grace 3.11 TensorFlow 2.16.2 12.3 525+
mace 3.11 torch 2.7.1 12.6 525+
matris 3.11 torch 2.12.1 13.0 580+
mattersim 3.10 torch 2.6.0 12.4 525+
nequip 3.11 torch 2.9.1 12.8 570+ compiled for your GPU at install (OpenEquivariance)
nequix 3.11 torch 2.10.0 12.6 525+
orb 3.11 torch 2.7.1 12.6 525+
pet 3.11 torch 2.9.1 12.8 570+ 2.8 GB checkpoint
sevennet 3.11 torch 2.7.1 12.6 525+ OpenEquivariance kernels built at install
tace 3.11 torch 2.11.0 13.0 580+
uma 3.11 torch 2.8.0 12.8 570+ gated weights; UMA-m-1p1-* needs 32 GB+ host RAM

Versions come from the lock files in envs/locks/; driver floors are the approximate Linux minimums for each CUDA build.

Common problems

  • GRACE weights: GRACE-2L-OAM (about 100 MB) comes from upstream's Hugging Face copy, checked against the recorded sha256, and an interrupted download resumes where it stopped. If Hugging Face is unreachable it falls back to grace_models and the upstream ICAMS share, which has served a few KiB/s (hours for the file) with no resume -- let that run finish undisturbed.

  • Driver too old for a CUDA 13.0 env (dpa4, matris, tace): torch.cuda.is_available() is False. Upgrade the driver, or use a host whose driver matches the build. A CPU run is not a general fallback here: these models' inference lines pin the GPU, and the hub refuses a CPU request rather than running on the GPU and reporting CPU.

  • A different GPU than the one you installed on: NequIP and Allegro load a model compiled for a specific GPU architecture. See GPU-architecture compilation.
  • Loading UMA-m-1p1-* dies with no error message: the checkpoint (11.2 GB) is loaded into host RAM first, and the kernel's out-of-memory killer ends the process (exit code 137). Use a machine with 32 GB or more, or a UMA-s-* model.
  • GRACE fine-tuning on WSL crashes right after Compiled cluster using XLA (segmentation fault, no Python traceback): rerun with --set jit_compile=false. Native Linux runs the default (jit_compile=true).
  • Gated models (UMA, eSEN) need your own Hugging Face login — see Hugging Face token.