Releases

This page describes how to install and use our release artifacts for ROCm and external builds like PyTorch and JAX. We produce build artifacts as part of our Continuous Integration (CI) build/test workflows as well as release artifacts as part of Continuous Delivery (CD) nightly releases.

For the development status of GPU architecture support in TheRock, please see SUPPORTED_GPUS.md which tracks release readiness for each AMD GPU architecture.

Important

These instructions assume familiarity with how to use ROCm. Please see https://rocm.docs.amd.com/ for general information about the ROCm software platform.

Prerequisites:

We recommend installing the latest AMDGPU driver on Linux and Adrenaline driver on Windows
Linux users, please be aware of Configuring permissions for GPU access needed for ROCm

Table of contents:

Multi-arch releases
Per-family releases
Verifying your installation

Multi-arch releases

Important

We are introducing multi-arch releases with #3323. Rather than build ROCm for GPU family subsets like the per-family releases, these multi-arch releases build all GPU architectures together and split GPU-specific code (kernel packs) from architecture-neutral host code as a packaging step.

This new setup will streamline package installation, so please note the differences in the install instructions.

Key differences from per-family releases:

One index URL for all GPUs: select your target with a pip extra like [device-gfx942] instead of finding a per-family index URL
Broader GPU support: adding support for a new GPU target is just one more device package, so more GPUs can be supported without impacting build times or download sizes for other targets
Smaller downloads: kernels downloads can be scoped to a single GPU instead of always being scoped to a family or "all"

Multi-arch release status

Platform	ROCm	PyTorch
Linux
Windows

Package availability:

Package type	Linux	Windows
ROCm Python packages	✅ Available	✅ Available
PyTorch Python packages	✅ Available Torch versions 2.10 and 2.11 only, other versions pending #4768 Missing flash attention pending #4969	✅ Available Missing flash attention pending #4969
JAX Python packages	🟠 Planned	-
ROCm tarballs	✅ Available	✅ Available
Native Linux packages	✅ Available	🟠 Planned (#1987)

Installing multi-arch ROCm Python packages

Nightly releases of ROCm and related Python packages are published to a unified index at https://rocm.nightlies.amd.com/whl-multi-arch/.

Tip

We highly recommend working within a Python virtual environment:

python -m venv .venv
source .venv/bin/activate

Multiple virtual environments can be present on a system at a time, allowing you to switch between them at will.

Warning

If you really want a system-wide install, you can pass --break-system-packages to pip outside a virtual enivornment. In this case, commandline interface shims for executables are installed to /usr/local/bin, which normally has precedence over /usr/bin and might therefore conflict with a previous installation of ROCm.

We provide several Python packages which together form the complete ROCm SDK. In multi-arch releases, GPU-specific device code is split into separate rocm-sdk-device-{target} packages.

See ROCm Python Packaging via TheRock for information about each package.
The packages are defined in the build_tools/packaging/python/templates/ directory.

Package name	Description
`rocm`	Primary sdist meta package that dynamically determines other deps
`rocm-sdk-core`	OS-specific core of the ROCm SDK (e.g. compiler and utility tools)
`rocm-sdk-libraries`	OS-specific libraries (architecture-neutral host code)
`rocm-sdk-device-{target}`	GPU-specific device code (e.g. `rocm-sdk-device-gfx942`)
`rocm-sdk-devel`	OS-specific development tools

Install ROCm with device support for your GPU using the unified index:

# Replace device-gfx942 with your GPU, see the section below for details
pip install --index-url https://rocm.nightlies.amd.com/whl-multi-arch/ "rocm[libraries,device-gfx942]"

After installing, verify your installation:

rocm-sdk test

Supported Python `[device-*]` install extras

For packages which include device-specific code (such as rocm, torch, and torchvision), support for individual devices can be installed using the corresponding device-* extra from the table below. See also the GPU architecture specs for a full list of supported AMD GPUs.

Product Name	GFX Target	Device Extra
AMD Instinct MI355X / MI350X	gfx950	`device-gfx950`
AMD Instinct MI325X / MI300X / MI300A	gfx942	`device-gfx942`
AMD Instinct MI250X / MI250 / MI210	gfx90a	`device-gfx90a`
AMD Instinct MI100	gfx908	`device-gfx908`
AMD Instinct MI60 / MI50, Radeon Pro VII, Radeon VII	gfx906	`device-gfx906`
AMD Instinct MI25	gfx900	`device-gfx900`
AMD Radeon RX 9070 / XT, AI PRO R9700 / R9600D	gfx1201	`device-gfx1201`
AMD Radeon RX 9060 / XT	gfx1200	`device-gfx1200`
AMD Radeon 820M iGPU	gfx1153	`device-gfx1153`
AMD Ryzen AI 7 350	gfx1152	`device-gfx1152`
AMD Ryzen AI Max+ PRO 395	gfx1151	`device-gfx1151`
AMD Ryzen AI 9 HX 375	gfx1150	`device-gfx1150`
AMD Ryzen 7 7840U / Ryzen 9 270	gfx1103	`device-gfx1103`
AMD Radeon RX 7600	gfx1102	`device-gfx1102`
AMD Radeon RX 7800 XT / 7700 XT, PRO V710 / W7700	gfx1101	`device-gfx1101`
AMD Radeon RX 7900 XTX / 7900 XT, PRO W7900 / W7800	gfx1100	`device-gfx1100`
AMD Radeon RX 6900 XT / 6800 XT, PRO W6800 / V620	gfx1030	`device-gfx1030`
AMD Radeon RX 6750 XT / 6700 XT	gfx1031	`device-gfx1031`
AMD Radeon RX 6600 XT / 6600, PRO W6600	gfx1032	`device-gfx1032`
AMD Van Gogh iGPU	gfx1033	`device-gfx1033`
AMD Radeon RX 6500 XT	gfx1034	`device-gfx1034`
AMD Radeon 680M iGPU	gfx1035	`device-gfx1035`
AMD Raphael iGPU	gfx1036	`device-gfx1036`
AMD Radeon RX 5700 / XT	gfx1010	`device-gfx1010`
AMD Radeon Pro V520	gfx1011	`device-gfx1011`
AMD Radeon Pro W5500	gfx1012	`device-gfx1012`

The Python `[device-all]` install extra

A [device-all] extra is also provided which installs device code for all GPUs.

Warning

The [device-all] extra may not work consistently for nightly releases because packages are promoted per-target as they pass tests. If tests are still running or if they failed for an individual target, this extra will not be able to find all required packages.

We also publish untested packages to the nightly "whl-staging-multi-arch" index which is not affected by this limitation.

Package index	Safe to use `[device-all]`?
https://rocm.nightlies.amd.com/whl-multi-arch/	❌ No (some packages may not be available)
https://rocm.nightlies.amd.com/whl-staging-multi-arch/	✅ Yes (index includes all packages, even if tests fail)

Installing multi-arch PyTorch Python packages

Install PyTorch with ROCm support using the same unified index:

# Replace device-gfx942 with your GPU, see the section above for details
# Note: we'll recommend 'whl-multi-arch' instead of 'whl-staging-multi-arch'
#       as soon as we test run automate tests on these packages
pip install --index-url https://rocm.nightlies.amd.com/whl-staging-multi-arch/ \
    "torch[device-gfx942]" "torchvision[device-gfx942]" torchaudio

# Optional additional packages on Linux:
#   apex

Tip

The device extras install GPU-specific packages like amd-torch-device-gfx1100 which contain GPU-specific kernels and depend on rocm-sdk-device-gfx1100. The compatible ROCm packages are installed automatically, you do not need to install ROCm separately:

pip install --index-url https://rocm.nightlies.amd.com/whl-staging-multi-arch/ \
    "torch[device-gfx1100]"

pip freeze  # with approximate download sizes:
# rocm-sdk-core==7.13.0a...              ~700 MB
# rocm-sdk-libraries==7.13.0a...         ~100 MB  (host code, shared across GPUs)
# rocm-sdk-device-gfx1100==7.13.0a...     ~50 MB  (only gfx1100 device code)
# torch==2.11.0+rocm...                  ~100 MB  (host code, shared across GPUs)
# amd-torch-device-gfx1100==2.11.0+...    ~50 MB  (only gfx1100 device code)
# Total:                                 ~1.1 GB
#
# For comparison, a similar per-family (non-multi-arch) torch wheel for
# gfx110X-all [gfx1100, gfx1101, gfx1102, gfx1103] is ~600 MB.

After installing, verify PyTorch can see your GPU:

import torch

print(torch.cuda.is_available())
# True
print(torch.cuda.get_device_name(0))
# e.g. AMD Radeon Pro W7900 Dual Slot

See external-builds/pytorch/README.md for more details on supported PyTorch versions and building from source.

Installing multi-arch tarballs

Standalone "ROCm SDK tarballs" are a flattened view of ROCm artifacts matching the familiar folder structure seen with system installs on Linux to /opt/rocm/ or on Windows via the HIP SDK:

install/
  .kpack/     # GPU-specific kernel packs (multi-arch only)
  bin/
  clients/
  include/
  lib/
  libexec/
  share/

Tarballs are just these raw files. They do not come with "install" steps such as setting environment variables.

Multi-arch tarballs separate GPU-specific kernel code into a .kpack/ directory. Two variants are available:

Per-family tarballs (e.g. therock-dist-linux-gfx110X-all-7.13.0a20260430.tar.gz) that include .kpack files only for one family.
Multiarch tarball (e.g. therock-dist-linux-multiarch-7.13.0a20260430.tar.gz) that include .kpack files for all supported targets.

Browse and download tarballs from https://rocm.nightlies.amd.com/tarball-multi-arch/.

To download and extract:

mkdir therock-tarball && cd therock-tarball

# Per-family (smaller, one GPU family):
wget https://rocm.nightlies.amd.com/tarball-multi-arch/therock-dist-linux-gfx110X-all-7.13.0a20260430.tar.gz

# Or multiarch (all GPUs):
wget https://rocm.nightlies.amd.com/tarball-multi-arch/therock-dist-linux-multiarch-7.13.0a20260430.tar.gz

mkdir install && tar -xf *.tar.gz -C install

After extraction, test the install:

./install/bin/rocminfo
ls install/.kpack/
# blas_lib_gfx1100.kpack  fft_lib_gfx1100.kpack  rand_lib_gfx1100.kpack  ...

Tip

You may also want to add parts of the install directory to your PATH or set other environment variables like ROCM_HOME.

See also this issue discussing relevant environment variables.

Installing multi-arch native Linux packages

In addition to Python wheels and tarballs, ROCm native Linux packages are published for Debian-based and RPM-based distributions via the multi-arch pipeline.

Warning

These builds are primarily intended for development and testing and are currently unsigned.

Multi-arch native packages use a simplified package model compared to the per-family native packages:

Package name	Description
`amdrocm`	Installs all base ROCm libraries and runtime support for all supported GPU architectures
`amdrocm-core-sdk`	Installs the full ROCm SDK including runtime, development tools, and headers for all supported GPU architectures

Tip

To find the latest available release, browse the index pages:

Debian packages: https://rocm.nightlies.amd.com/packages-multi-arch/deb/
RPM packages: https://rocm.nightlies.amd.com/packages-multi-arch/rpm/

Look for directories in the format YYYYMMDD-<action-run-id> (e.g., 20260501-25200531110) and use the latest in the commands below.

Installing on Debian-based systems (Ubuntu, Debian, etc.)

# Step 1: Find the latest release from
#         https://rocm.nightlies.amd.com/packages-multi-arch/deb/
#         Look for directories like "20260501-25200531110"
# Step 2: Set the variable below
export RELEASE_ID=20260501-25200531110  # Replace with the latest date-runid

# Step 3: Add repository and install
sudo apt update
sudo apt install -y ca-certificates
echo "deb [trusted=yes] https://rocm.nightlies.amd.com/packages-multi-arch/deb/${RELEASE_ID} stable main" \
  | sudo tee /etc/apt/sources.list.d/rocm-multiarch-nightly.list
sudo apt update

# Install base runtime for all supported GPU architectures:
sudo apt install amdrocm
# Or install full SDK (runtime + dev tools + headers) for all supported GPU architectures:
sudo apt install amdrocm-core-sdk

Installing on RPM-based systems (RHEL, SLES, AlmaLinux, etc.)

# Step 1: Find the latest release from
#         https://rocm.nightlies.amd.com/packages-multi-arch/rpm/
#         Look for directories like "20260501-25200531110"
# Step 2: Set the variable below
export RELEASE_ID=20260501-25200531110  # Replace with the latest date-runid

# Step 3: Add repository and install
sudo dnf install -y ca-certificates
sudo tee /etc/yum.repos.d/rocm-multiarch-nightly.repo <<EOF
[rocm-multiarch-nightly]
name=ROCm Multi-Arch Nightly Repository
baseurl=https://rocm.nightlies.amd.com/packages-multi-arch/rpm/${RELEASE_ID}/x86_64
enabled=1
gpgcheck=0
priority=50
EOF

# Install base runtime for all supported GPU architectures:
sudo dnf clean all
sudo dnf install amdrocm
# Or install full SDK (runtime + dev tools + headers) for all supported GPU architectures:
sudo dnf install amdrocm-core-sdk

Note

To install support for a specific GPU architecture only, you can use the per-arch package variant (e.g., apt install amdrocm-gfx942 or dnf install amdrocm-gfx942). For a full list of supported GPU targets and their identifiers, see Supported Python [device-*] install extras.

Per-family releases

Per-family releases use GPU-family-specific index URLs — you choose the index URL that matches your GPU family, and all packages for that family are served from that URL.

Note

Multi-arch releases (above) are the newer approach and will soon replace per-family releases. Both are available during the transition.

Installing per-family releases using pip

We recommend installing ROCm and projects like PyTorch and JAX via pip, the Python package installer.

We currently support Python 3.10, 3.11, 3.12, 3.13, and 3.14 (PyTorch 2.9+ only).

Tip

We highly recommend working within a Python virtual environment:

python -m venv .venv
source .venv/bin/activate

Multiple virtual environments can be present on a system at a time, allowing you to switch between them at will.

Warning

Python packages release status

Important

Known issues with the Python wheels are tracked at #808.

Platform	ROCm Python packages	PyTorch Python packages	JAX Python packages
Linux
Windows			—

Index page listing

For now, rocm, torch, and jax packages are published to GPU-architecture-specific index pages and must be installed using an appropriate --find-links argument to pip. They may later be pushed to the Python Package Index (PyPI) or other channels using a process like https://wheelnext.dev/. Please check back regularly as these instructions will change as we migrate to official indexes and adjust project layouts.

Product Name	GFX Target	GFX Family	Install instructions
MI300A/MI300X	gfx942	gfx94X-dcgpu	rocm // torch // jax
MI350X/MI355X	gfx950	gfx950-dcgpu	rocm // torch // jax
AMD RX 7900 XTX	gfx1100	gfx110X-all	rocm // torch // jax
AMD RX 7800 XT	gfx1101	gfx110X-all	rocm // torch // jax
AMD RX 7700S / Framework Laptop 16	gfx1102	gfx110X-all	rocm // torch // jax
AMD Radeon 780M Laptop iGPU	gfx1103	gfx110X-all	rocm // torch // jax
AMD Strix Halo iGPU	gfx1151	gfx1151	rocm // torch // jax
AMD RX 9060 / XT	gfx1200	gfx120X-all	rocm // torch // jax
AMD RX 9070 / XT	gfx1201	gfx120X-all	rocm // torch // jax

Installing ROCm Python packages

We provide several Python packages which together form the complete ROCm SDK.

See ROCm Python Packaging via TheRock for information about the each package.
The packages are defined in the build_tools/packaging/python/templates/ directory.

Package name	Description
`rocm`	Primary sdist meta package that dynamically determines other deps
`rocm-sdk-core`	OS-specific core of the ROCm SDK (e.g. compiler and utility tools)
`rocm-sdk-libraries`	OS-specific libraries
`rocm-sdk-devel`	OS-specific development tools

Optional profiler package

A new optional package rocm-profiler is available, providing ROCm profiling tools:

ROCm Systems Profiler (rocprofiler-systems)
ROCm Compute Profiler (rocprofiler-compute)

Installing the profiler package

Install profiling tools via the meta package:

pip install "rocm[profiler]"

This will install:

rocm-sdk-core (required runtime + SDK)
rocm-profiler (profiling tools)

rocm for gfx94X-dcgpu

Supported devices in this family:

Product Name	GFX Target
MI300A/MI300X	gfx942

Install instructions:

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx94X-dcgpu/ "rocm[libraries,devel]"

rocm for gfx950-dcgpu

Supported devices in this family:

Product Name	GFX Target
MI350X/MI355X	gfx950

Install instructions:

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx950-dcgpu/ "rocm[libraries,devel]"

rocm for gfx110X-all

Supported devices in this family:

Product Name	GFX Target
AMD RX 7900 XTX	gfx1100
AMD RX 7800 XT	gfx1101
AMD RX 7700S / Framework Laptop 16	gfx1102
AMD Radeon 780M Laptop iGPU	gfx1103

Install instructions:

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/ "rocm[libraries,devel]"

rocm for gfx1151

Supported devices in this family:

Product Name	GFX Target
AMD Strix Halo iGPU	gfx1151

Install instructions:

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx1151/ "rocm[libraries,devel]"

rocm for gfx120X-all

Supported devices in this family:

Product Name	GFX Target
AMD RX 9060 / XT	gfx1200
AMD RX 9070 / XT	gfx1201

Install instructions:

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ "rocm[libraries,devel]"

Using ROCm Python packages

After installing the ROCm Python packages, you should see them in your environment:

pip freeze | grep rocm
# rocm==6.5.0rc20250610
# rocm-sdk-core==6.5.0rc20250610
# rocm-sdk-devel==6.5.0rc20250610
# rocm-sdk-libraries-gfx110X-all==6.5.0rc20250610

You should also see various tools on your PATH and in the bin directory:

which rocm-sdk
# .../.venv/bin/rocm-sdk

ls .venv/bin
# activate       amdclang++    hipcc      python                 rocm-sdk
# activate.csh   amdclang-cl   hipconfig  python3                rocm-smi
# activate.fish  amdclang-cpp  pip        python3.12             roc-obj
# Activate.ps1   amdflang      pip3       rocm_agent_enumerator  roc-obj-extract
# amdclang       amdlld        pip3.12    rocminfo               roc-obj-ls

The rocm-sdk tool can be used to inspect and test the installation:

$ rocm-sdk --help
usage: rocm-sdk {command} ...

ROCm SDK Python CLI

positional arguments:
  {path,test,version,targets,init}
    path                Print various paths to ROCm installation
    test                Run installation tests to verify integrity
    version             Print version information
    targets             Print information about the GPU targets that are supported
    init                Expand devel contents to initialize rocm[devel]

$ rocm-sdk test
...
Ran 22 tests in 8.284s
OK

$ rocm-sdk targets
gfx1100;gfx1101;gfx1102

To initialize the rocm[devel] package, use the rocm-sdk tool to eagerly expand development contents:

$ rocm-sdk init
Devel contents expanded to '.venv/lib/python3.12/site-packages/_rocm_sdk_devel'

These contents are useful for using the package outside of Python and lazily expanded on the first use when used from Python.

Once you have verified your installation, you can continue to use it for standard ROCm development or install PyTorch, JAX, or another supported Python ML framework.

Installing PyTorch Python packages

Using the index pages listed above, you can also install torch, torchaudio, torchvision, and apex.

Note

By default, pip will install the latest stable versions of each package.

If you want to allow installing prerelease versions, use the --pre
If you want to install other versions, take note of the compatibility matrix:

torch version torchaudio version torchvision version apex version

2.10 2.10 0.25 1.10.0

2.9 2.9 0.24 1.9.0

2.8 2.8 0.23 1.8.0

For example, torch 2.8 and compatible wheels can be installed by specifying
```
torch==2.8 torchaudio==2.8 torchvision==0.23 apex==1.8.0
```
See also

torch version	torchaudio version	torchvision version	apex version
2.10	2.10	0.25	1.10.0
2.9	2.9	0.24	1.9.0
2.8	2.8	0.23	1.8.0

Warning

The torch packages depend on rocm[libraries], so the compatible ROCm packages should be installed automatically for you and you do not need to explicitly install ROCm first. If ROCm is already installed this may result in a downgrade if the torch wheel to be installed requires a different version.

Tip

If you previously installed PyTorch with the pytorch-triton-rocm package, please uninstall it before installing the new packages:

pip uninstall pytorch-triton-rocm

The triton package is now named triton.

torch for gfx94X-dcgpu

Supported devices in this family:

Product Name	GFX Target
MI300A/MI300X	gfx942

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx94X-dcgpu/ torch torchaudio torchvision
# Optional additional packages on Linux:
#   apex

torch for gfx950-dcgpu

Supported devices in this family:

Product Name	GFX Target
MI350X/MI355X	gfx950

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx950-dcgpu/ torch torchaudio torchvision
# Optional additional packages on Linux:
#   apex

torch for gfx110X-all

Supported devices in this family:

Product Name	GFX Target
AMD RX 7900 XTX	gfx1100
AMD RX 7800 XT	gfx1101
AMD RX 7700S / Framework Laptop 16	gfx1102
AMD Radeon 780M Laptop iGPU	gfx1103

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/ torch torchaudio torchvision
# Optional additional packages on Linux:
#   apex

torch for gfx1151

Supported devices in this family:

Product Name	GFX Target
AMD Strix Halo iGPU	gfx1151

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx1151/ torch torchaudio torchvision
# Optional additional packages on Linux:
#   apex

torch for gfx120X-all

Supported devices in this family:

Product Name	GFX Target
AMD RX 9060 / XT	gfx1200
AMD RX 9070 / XT	gfx1201

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ torch torchaudio torchvision
# Optional additional packages on Linux:
#   apex

Using PyTorch Python packages

After installing the torch package with ROCm support, PyTorch can be used normally:

import torch

print(torch.cuda.is_available())
# True
print(torch.cuda.get_device_name(0))
# e.g. AMD Radeon Pro W7900 Dual Slot

See also the Testing the PyTorch installation instructions in the AMD ROCm documentation.

Installing JAX Python packages

Using the index pages listed above, you can also install jaxlib, jax_rocm7_plugin, and jax_rocm7_pjrt.

Note

By default, pip will install the latest stable versions of each package.

If you want to install other versions, the currently supported versions are:

jax version jaxlib version

0.9.1 0.9.1 (upstream)

0.8.2 0.8.2

0.8.0 0.8.0

See also
- Supported JAX versions in TheRock

jax version	jaxlib version
0.9.1	0.9.1 (upstream)
0.8.2	0.8.2
0.8.0	0.8.0

Warning

Unlike PyTorch, the JAX wheels do not automatically install rocm[libraries] as a dependency. You must have ROCm installed separately via a tarball installation.

Important

The jax package itself is not published to the TheRock index. After installing jaxlib, jax_rocm7_plugin, and jax_rocm7_pjrt from the GPU-family index, install jax from PyPI:

pip install jax

jax for gfx94X-dcgpu

Supported devices in this family:

Product Name	GFX Target
MI300A/MI300X	gfx942

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx94X-dcgpu/ jaxlib jax_rocm7_plugin jax_rocm7_pjrt
# Install jax from PyPI
pip install jax

jax for gfx950-dcgpu

Supported devices in this family:

Product Name	GFX Target
MI350X/MI355X	gfx950

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx950-dcgpu/ jaxlib jax_rocm7_plugin jax_rocm7_pjrt
# Install jax from PyPI
pip install jax

jax for gfx110X-all

Supported devices in this family:

Product Name	GFX Target
AMD RX 7900 XTX	gfx1100
AMD RX 7800 XT	gfx1101
AMD RX 7700S / Framework Laptop 16	gfx1102
AMD Radeon 780M Laptop iGPU	gfx1103

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/ jaxlib jax_rocm7_plugin jax_rocm7_pjrt
# Install jax from PyPI
pip install jax

jax for gfx1151

Supported devices in this family:

Product Name	GFX Target
AMD Strix Halo iGPU	gfx1151

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx1151/ jaxlib jax_rocm7_plugin jax_rocm7_pjrt
# Install jax from PyPI
pip install jax

jax for gfx120X-all

Supported devices in this family:

Product Name	GFX Target
AMD RX 9060 / XT	gfx1200
AMD RX 9070 / XT	gfx1201

pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ jaxlib jax_rocm7_plugin jax_rocm7_pjrt
# Install jax from PyPI
pip install jax

Using JAX Python packages

After installing the JAX packages with ROCm support, JAX can be used normally:

import jax

print(jax.devices())
# [RocmDevice(id=0)]

For building JAX from source or running the full JAX test suite, see the external-builds/jax README.

Installing from tarballs

Standalone "ROCm SDK tarballs" are a flattened view of ROCm artifacts matching the familiar folder structure seen with system installs on Linux to /opt/rocm/ or on Windows via the HIP SDK:

install/  # Extracted tarball location, file path of your choosing
  .info/
  bin/
  clients/
  include/
  lib/
  libexec/
  share/

Tarballs are just these raw files. They do not come with "install" steps such as setting environment variables.

Warning

Tarballs and per-commit CI artifacts are primarily intended for developers and CI workflows.

For most users, we recommend installing via package managers:

Installing multi-arch releases using pip
Installing per-family releases using pip
Installing from native packages

Browsing release tarballs

Release tarballs are uploaded to the following locations:

Tarball index	S3 bucket	Description
https://repo.amd.com/rocm/tarball/	(not publicly accessible)	Stable releases
https://rocm.nightlies.amd.com/tarball/	`therock-nightly-tarball`	Nightly builds from the default development branch
https://rocm.prereleases.amd.com/tarball/	(not publicly accessible)	⚠️ Prerelease builds for QA testing ⚠️
https://rocm.devreleases.amd.com/tarball/	`therock-dev-tarball`	⚠️ Development builds from project maintainers ⚠️

Manual tarball extraction

To download a tarball and extract it into place manually:

mkdir therock-tarball && cd therock-tarball
# For example...
wget https://rocm.nightlies.amd.com/tarball/therock-dist-linux-gfx110X-all-7.12.0a20260202.tar.gz
mkdir install && tar -xf *.tar.gz -C install

Automated tarball extraction

For more control over artifact installation—including per-commit CI builds, specific release versions, the latest nightly release, and component selection—see the Installing Artifacts developer documentation. The install_rocm_from_artifacts.py script can be used to install artifacts from a variety of sources.

Using installed tarballs

After installing (downloading and extracting) a tarball, you can test it by running programs from the bin/ directory:

ls install
# bin  include  lib  libexec  llvm  share

# Now test some of the installed tools:
./install/bin/rocminfo
./install/bin/test_hip_api

Tip

You may also want to add parts of the install directory to your PATH or set other environment variables like ROCM_HOME.

See also this issue discussing relevant environment variables.

Tip

After extracting a tarball, metadata about which commits were used to build TheRock can be found in the share/therock/therock_manifest.json file:

cat install/share/therock/therock_manifest.json
# {
#   "the_rock_commit": "567dd890a3bc3261ffb26ae38b582378df298374",
#   "submodules": [
#     {
#       "submodule_name": "half",
#       "submodule_path": "base/half",
#       "submodule_url": "https://github.com/ROCm/half.git",
#       "pin_sha": "207ee58595a64b5c4a70df221f1e6e704b807811",
#       "patches": []
#     },
#     ...

Installing from native packages

In addition to Python wheels and tarballs, ROCm native Linux packages are published for Debian-based and RPM-based distributions.

Warning

These builds are primarily intended for development and testing and are currently unsigned.

Native packages release status

Platform	Native packages
Linux
Windows	(Coming soon)

GPU family and package mapping

Product Name	GFX Target	GFX Family	Runtime Package	Development Package
MI300A/MI300X	gfx942	gfx94X	amdrocm-gfx94x	amdrocm-core-sdk-gfx94x
MI350X/MI355X	gfx950	gfx950	amdrocm-gfx950	amdrocm-core-sdk-gfx950
AMD RX 7900 XTX	gfx1100	gfx110x	amdrocm-gfx110x	amdrocm-core-sdk-gfx110x
AMD RX 7800 XT	gfx1101	gfx110x	amdrocm-gfx110x	amdrocm-core-sdk-gfx110x
AMD RX 7700S / Framework Laptop 16	gfx1102	gfx110x	amdrocm-gfx110x	amdrocm-core-sdk-gfx110x
AMD Radeon 780M Laptop iGPU	gfx1103	gfx110x	amdrocm-gfx110x	amdrocm-core-sdk-gfx110x
AMD Strix Point iGPU	gfx1150	gfx1150	amdrocm-gfx1150	amdrocm-core-sdk-gfx1150
AMD Strix Halo iGPU	gfx1151	gfx1151	amdrocm-gfx1151	amdrocm-core-sdk-gfx1151
AMD Fire Range iGPU	gfx1152	gfx1152	amdrocm-gfx1152	amdrocm-core-sdk-gfx1152
AMD Strix Halo XT	gfx1153	gfx1153	amdrocm-gfx1153	amdrocm-core-sdk-gfx1153
AMD RX 9060 / XT	gfx1200	gfx120X	amdrocm-gfx120x	amdrocm-core-sdk-gfx120x
AMD RX 9070 / XT	gfx1201	gfx120X	amdrocm-gfx120x	amdrocm-core-sdk-gfx120x
Radeon VII	gfx906	gfx906	amdrocm-gfx906	amdrocm-core-sdk-gfx906
MI100	gfx908	gfx908	amdrocm-gfx908	amdrocm-core-sdk-gfx908
MI200 series	gfx90a	gfx90a	amdrocm-gfx90a	amdrocm-core-sdk-gfx90a
AMD RX 5700 XT	gfx1010	gfx101x	amdrocm-gfx101x	amdrocm-core-sdk-gfx101x
AMD RX 6900 XT	gfx1030	gfx103x	amdrocm-gfx103x	amdrocm-core-sdk-gfx103x
AMD RX 6800 XT	gfx1031	gfx103x	amdrocm-gfx103x	amdrocm-core-sdk-gfx103x

Tip

To find the latest available release:

Step 1: Browse the index pages:
- Debian packages: https://rocm.nightlies.amd.com/deb/
- RPM packages: https://rocm.nightlies.amd.com/rpm/
Step 2: Look for directories in the format YYYYMMDD-<action-run-id> (e.g., 20260310-12345678)
Step 3: Use the latest date in the installation commands below

Installing on Debian-based systems (Ubuntu, Debian, etc.)

# Step 1: Find the latest release from https://rocm.nightlies.amd.com/deb/
#         Look for directories like "20260310-12345678"
# Step 2: Look at the "GPU family and package mapping" table above to find
#         the GFX Family for your GPU (e.g., gfx94x, gfx110x, gfx1151)
# Step 3: Set the variables below

export RELEASE_ID=20260310-12345678  # Replace with actual date-runid
export GFX_ARCH=gfx110x              # Replace with GFX Family from the mapping table

# Step 4: Add repository and install
sudo apt update
sudo apt install -y ca-certificates
echo "deb [trusted=yes] https://rocm.nightlies.amd.com/deb/${RELEASE_ID} stable main" \
  | sudo tee /etc/apt/sources.list.d/rocm-nightly.list
sudo apt update
sudo apt install amdrocm-core-sdk-${GFX_ARCH}
# If only runtime is needed, install amdrocm-${GFX_ARCH} instead

Installing on RPM-based systems (RHEL, SLES, AlmaLinux etc.)

Note

The following instructions are for RHEL-based operating systems.

# Step 1: Find the latest release from https://rocm.nightlies.amd.com/rpm/
#         Look for directories like "20260310-12345678"
# Step 2: Look at the "GPU family and package mapping" table above to find
#         the GFX Family for your GPU (e.g., gfx94x, gfx110x, gfx1151)
# Step 3: Set the variables below

export RELEASE_ID=20260310-12345678  # Replace with actual date-runid
export GFX_ARCH=gfx110x              # Replace with GFX Family from the mapping table

# Step 4: Add repository and install
sudo dnf install -y ca-certificates
sudo tee /etc/yum.repos.d/rocm-nightly.repo <<EOF
[rocm-nightly]
name=ROCm Nightly Repository
baseurl=https://rocm.nightlies.amd.com/rpm/${RELEASE_ID}/x86_64
enabled=1
gpgcheck=0
priority=50
EOF
sudo dnf clean all
sudo dnf install amdrocm-core-sdk-${GFX_ARCH}
# If only runtime is needed, install amdrocm-${GFX_ARCH} instead

Verifying your installation

After installing ROCm via any of the methods above, you can verify that your GPU is properly recognized.

Verifying installation on Linux

GPU status on Linux can be checked via either:

rocminfo
# or
amd-smi

Verifying installation on Windows

GPU status on Windows can be checked via

hipInfo.exe

Additional installation troubleshooting

If your GPU is not recognized or you encounter issues:

Linux users: Check system logs using dmesg | grep amdgpu for specific error messages
Review memory allocation settings (see the FAQ for GTT configuration on unified memory systems)
Ensure you have the latest AMDGPU driver on Linux or Adrenaline driver on Windows
For platform-specific troubleshooting when using PyTorch or JAX, see:

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RELEASES.md

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File metadata and controls

Releases

Multi-arch releases

Multi-arch release status

Installing multi-arch ROCm Python packages

Supported Python [device-*] install extras

The Python [device-all] install extra

Installing multi-arch PyTorch Python packages

Installing multi-arch tarballs

Installing multi-arch native Linux packages

Installing on Debian-based systems (Ubuntu, Debian, etc.)

Installing on RPM-based systems (RHEL, SLES, AlmaLinux, etc.)

Per-family releases

Installing per-family releases using pip

Python packages release status

Index page listing

Installing ROCm Python packages

Optional profiler package

Installing the profiler package

rocm for gfx94X-dcgpu

rocm for gfx950-dcgpu

rocm for gfx110X-all

rocm for gfx1151

rocm for gfx120X-all

Using ROCm Python packages

Installing PyTorch Python packages

torch for gfx94X-dcgpu

torch for gfx950-dcgpu

torch for gfx110X-all

torch for gfx1151

torch for gfx120X-all

Using PyTorch Python packages

Installing JAX Python packages

jax for gfx94X-dcgpu

jax for gfx950-dcgpu

jax for gfx110X-all

jax for gfx1151

jax for gfx120X-all

Using JAX Python packages

Installing from tarballs

Browsing release tarballs

Manual tarball extraction

Automated tarball extraction

Using installed tarballs

Installing from native packages

Native packages release status

GPU family and package mapping

Installing on Debian-based systems (Ubuntu, Debian, etc.)

Installing on RPM-based systems (RHEL, SLES, AlmaLinux etc.)

Verifying your installation

Verifying installation on Linux

Verifying installation on Windows

Additional installation troubleshooting

Supported Python `[device-*]` install extras

The Python `[device-all]` install extra