nbxtensor 0.0.8, from the
v0.0.8tag of github.com/NeuroBrix/nbxtensor. Files nameddocs/nbxtensor/…in this text are not part of the nbxtensor repository at this version.
pip install nbxtensor
nbxtensor never needs torch: it is not a dependency, not even an optional one, and the library and its kernels run in
an environment where torch is not installed (proven on Metal, 2026-10-29: tools/torch_free_proof.py records every
attempt to import torch, caught ones included, and recorded none). Python 3.10 or later; its dependencies are numpy,
pyyaml and Triton (on a Mac, pyobjc-framework-Metal and the Triton install-metal brings).
pip install nbxtensor
Linux, NVIDIA (CUDA). Triton and the CUDA runtime library come from PyPI with the install above; the NVIDIA driver
is the only other requirement.
Linux, AMD (ROCm) and Intel (Level Zero). The system's GPU runtime and a Triton that compiles for the card are
needed: see Install on AMD / Intel GPUs.
Which GPU, and what is missing. python -m nbxtensor doctor names the backend in force and, for every NVIDIA, AMD
or Intel GPU on the PCI bus, whether its runtime library and its Triton backend are installed — with the vendor's
install commands when one is not.
From the NeuroBrix repository (contributors): pip install -e nbxtensor.
macOS, Apple silicon (Metal). PyPI publishes no Triton for macOS. install-metal installs NeuroBrix's Triton
build for Apple GPUs, NeuroBrix/triton-ext (Triton built with
TRITON_EXT_ENABLED=1 and the nbx-triton-apple backend; upstream:
triton-lang/triton-ext#126); each wheel is checked against the
sha256 written in the library (Python 3.12, macOS 15 or later):
pip install nbxtensor
python -m nbxtensor install-metal
Xcode's Metal toolchain is the other requirement (xcrun metal must find the real compiler, not Xcode's placeholder:
TOOLCHAINS=Metal); python -m nbxtensor doctor says what is missing. To build Triton and its Metal backend yourself
instead: Triton (github.com/triton-lang/triton) built with TRITON_EXT_ENABLED=1, then triton-ext's backend/AppleGPU
installed into the same environment.
nbxtensor.dtype and nbxtensor.shape need neither a device nor Triton.
The library speaks to the card through the vendor's own runtime library, loaded at run time, and compiles its kernels
with the Triton backend for that card. When a card is present and either is missing, the library refuses with the
steps below (the same text python -m nbxtensor doctor prints). The commands are the vendors' (Ubuntu 24.04).
AMD (ROCm, HIP). The ROCm runtime, from AMD's
quick start:
wget https://repo.radeon.com/amdgpu-install/6.4.3/ubuntu/noble/amdgpu-install_6.4.60403-1_all.deb
sudo apt install ./amdgpu-install_6.4.60403-1_all.deb
sudo apt update && sudo apt install rocm
sudo usermod -a -G render,video $LOGNAME # then log out and in (or reboot)
pip install nbxtensor # the Triton wheel on PyPI carries the AMD backend
Targets: CDNA (gfx908, gfx90a, gfx942, gfx950) and RDNA 3 / 4 (gfx11xx, gfx120x); the hardware profile states the
card's gfx arch. Triton 3.8 compiles the library's kernels for these targets on a machine without the card (the
library's tests do it).
Intel (Level Zero). Intel's GPU driver, from Intel's
package guide, then
Intel's Triton build (the PyPI Triton has no Intel backend):
sudo add-apt-repository -y ppa:kobuk-team/intel-graphics
sudo apt-get install -y libze-intel-gpu1 libze1 intel-opencl-icd clinfo intel-gsc
sudo usermod -a -G render $LOGNAME # then log out and in
pip install triton-xpu --index-url https://download.pytorch.org/whl/xpu
pip install nbxtensor
Devices: Data Center GPU Max (Ponte Vecchio), Arc A-series and Data Center GPU Flex (Alchemist), Arc B-series
(Battlemage), Core Ultra 200V graphics (Lunar Lake); the card's PCI id selects its device file.
Found a bug on your hardware? Open an issue — we fix fast.