# Installing ROCm on Ubuntu for Radeon GPUs

> Step-by-step guide to installing ROCm 7.14 on Ubuntu 26.04 for AMD Radeon (RDNA2/RDNA3) GPUs – from apt setup to rocminfo verification.

*Source: https://velstech.net/install-rocm-ubuntu · Updated: 2026-08-25 · Category: Tutorials · Tags: ROCm, Radeon, GPU*

*Markdown version of [Installing ROCm on Ubuntu for Radeon GPUs](https://velstech.net/install-rocm-ubuntu). [Read the full guide with interactive tools](https://velstech.net/install-rocm-ubuntu).*
*Also as Markdown: [Hindi](https://velstech.net/install-rocm-ubuntu.hi.md) · [Tamil](https://velstech.net/install-rocm-ubuntu.ta.md).*

---

AMD's ROCm (Radeon Open Compute) platform lets you run GPU-accelerated workloads like
PyTorch, TensorFlow, and LLM inference on AMD GPUs. If you have a supported Radeon
card – RDNA2 (RX 6000 series / gfx1030) or RDNA3 (RX 7000 series / gfx1100) – this
guide walks you through installing ROCm 7.14 on Ubuntu 26.04 LTS.

I tested this on a **Radeon RX 6800M** (gfx1031, RDNA2), and everything
below is what actually worked. The same steps apply to most RDN-based cards.

## Prerequisites

- An AMD Radeon GPU from the supported list

- Ubuntu 26.04 LTS (Resolute Raccoon) installed

- An internet connection

- A user account with sudo privileges

## Step 1 – Install the amdgpu-install script

ROCm uses the `amdgpu-install` script to handle the GPU driver, ROCm
runtime, and graphics components together. Download and install it from AMD's
repository:

```
sudo apt update
wget https://repo.radeon.com/amdgpu-install/31.40.1/ubuntu/resolute/amdgpu-install_31.40.1.314001-1_all.deb
sudo apt install ./amdgpu-install_31.40.1.314001-1_all.deb
```

Replace `resolute` with `noble` (24.04) or `jammy`
(22.04) if you're on a different Ubuntu version.

## Step 2 – Install ROCm and the GPU driver

Now run `amdgpu-install` with the appropriate use case and GPU target.

```
sudo amdgpu-install --usecase=rocm,graphics --gfxversion=gfx1031
```

**Important:** Use your GPU's specific `gfx` target instead of
`auto`. The auto-detection can sometimes miss the right architecture, so
specifying it explicitly gives a cleaner install. Find your GPU's gfx version:

- RDNA2 (RX 6000 series): gfx1030, gfx1031, gfx1032

- RDNA3 (RX 7000 series): gfx1100, gfx1101, gfx1102

- RDNA3.5 (Ryzen AI 300): gfx1150, gfx1151

Not sure? Run `rocminfo` after installing (or look up your card's
architecture on AMD's site).

> Device family note: When the installer prompts you to select a
> device family, you have two options:
>
>
> All – installs runtime support for every AMD GPU architecture.
> Good if you're developing cross-platform or testing on multiple cards.
>
>
> AMD Radeon – targets only consumer Radeon GPUs. This is the
> leaner, more focused choice for a single Radeon card.
>
>
> If you're just using your own machine, pick AMD Radeon. If you're
> building something that may run on Instinct or other architectures later, pick
> All.

## Step 3 – Install additional libraries

Some ROCm tools need the `libatomic` and `libquadmath`
libraries to run correctly. Install them now so you don't hit a cryptic error
later:

```
sudo apt install libatomic1 libquadmath0
```

## Step 4 – Add your user to the render and video groups

GPU access is controlled through Linux groups. Your user needs to be in the
`render` and `video` groups to use the AMD GPU without
sudo:

```
sudo usermod -a -G render,video $LOGNAME
```

Log out and back in (or restart) for the group change to take effect.

## Step 5 – Reboot

A reboot ensures the kernel module (amdgpu) and ROCk driver are loaded:

```
sudo reboot
```

## Step 6 – Verify the installation

After rebooting, run `rocminfo` to confirm your GPU is detected:

```
rocminfo
```

You should see your GPU listed with its name, gfx architecture, and compute units.
A healthy output looks like this:

```
*******
Agent 2
*******
  Name:                    gfx1031
  Marketing Name:          AMD Radeon RX 6800M
  Compute Unit:            40
  Device Type:             GPU
  ...
```

You can also check that the kernel driver is loaded:

```
ls /dev/kfd   # should exist
ls /dev/dri/  # should show renderD* nodes
```

## What's next?

With ROCm installed, you can now:

- Install PyTorch with ROCm support –
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1

- Run LLMs locally – tools like Ollama, LM Studio, and
text-generation-webui all support ROCm out of the box.

- Use HIP for GPU programming – AMD's CUDA-compatible framework.

If the installer didn't detect your GPU, double-check the `--gfxversion`
flag and try the **All** device family option. If you run into issues,
AMD's [official ROCm install guide](https://rocm.docs.amd.com/en/latest/install/rocm.html)
has the full details.

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*VelsTech – technology explained for everyone. Original: https://velstech.net/install-rocm-ubuntu*