AMD RX 7800XT LLM Setup

Running LLMs on AMD RX 7800 XT (ROCm Guide)

Guide is provided by Ian Wirtz

Recommended: LM Studio (lms) tends to give the best results, but Ollama is a viable alternative.

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Prerequisites

Step 1 - Install rocm-hip-runtime

Before LM Studio or Ollama can use your GPU via ROCm, the system needs the base user-space HIP libraries to communicate with the kernel driver.

Arch Linux / CachyOS:

sudo pacman -S rocm-hip-runtime

Ubuntu / Debian:

sudo apt install rocm-hip-runtime

Fedora:

sudo dnf install rocm-hip-runtime

GPU access permissions: Your user must belong to the render and video groups. Check with:

groups

If either group is missing, add yourself:

sudo usermod -aG video,render $USER

You must fully log out and back in (or reboot) for the group change to take effect.

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Step 2 - Install rocm-smi (may be optional)

On fast-moving distributions like Arch, the management tools are not always pulled in as a strict dependency of rocm-hip-runtime. Install them explicitly to avoid library version mismatches.

Arch Linux / CachyOS:

sudo pacman -S rocm-smi-lib

Ubuntu / Debian:

Debian splits the CLI tool and its runtime library into separate packages.

# Command-line monitoring tool
sudo apt update && sudo apt install rocm-smi

# Runtime libraries
sudo apt update && sudo apt install librocm-smi64-1

Tip: If the exact package name is uncertain, type sudo apt install librocm-smi64 and press Tab to autocomplete the current version suffix.

Fedora:

# CLI tool
sudo dnf install rocm-smi

# C/C++ development libraries and headers (equivalent to rocm-smi-lib on Arch)
sudo dnf install rocm-smi-devel

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Running a Model

Step 3 - Load a Model with ROCm Acceleration

When invoking lms load, pass the hardware acceleration flags explicitly. The --gpu max flag instructs the runtime to load the entire model into VRAM.

HSA_OVERRIDE_GFX_VERSION=11.0.0 lms load tulu-3.1-8b-supernova --context-length 8192 --gpu max

The HSA_OVERRIDE_GFX_VERSION=11.0.0 prefix tells the ROCm stack to treat the RX 7800 XT as a natively supported compute target, bypassing silent fallback failures.

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Step 4 - Make the Configuration Permanent

To avoid prefixing every command with the environment variable, add it to your shell profile.

Bash:

echo 'export HSA_OVERRIDE_GFX_VERSION=11.0.0' >> ~/.bashrc
source ~/.bashrc

Fish (CachyOS default) - Option A: Universal Variable (recommended)

Set it once; Fish persists it automatically across reboots with no further configuration needed:

set -Ux HSA_OVERRIDE_GFX_VERSION 11.0.0

Fish - Option B: Explicit config file entry

echo 'set -gx HSA_OVERRIDE_GFX_VERSION 11.0.0' >> ~/.config/fish/config.fish
source ~/.config/fish/config.fish

Ollama (systemd service):

Because Ollama runs under its own ollama system user, the variable must be injected via a systemd drop-in:

sudo mkdir -p /etc/systemd/system/ollama.service.d
sudo nano /etc/systemd/system/ollama.service.d/override.conf

Paste the following, then save and exit (Ctrl+O, Enter, Ctrl+X):

[Service]
Environment="HSA_OVERRIDE_GFX_VERSION=11.0.0"

Then reload and restart the service:

sudo systemctl daemon-reload
sudo systemctl restart ollama

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Verification

Step 5 - Confirm the Kernel Compute Driver is Loaded

If a command hangs, the kernel compute layer (amdkfd) may not be initialised. Check whether the system exposes your GPU as a ROCm compute platform:

rocminfo

Scroll to the top of the output. If you see Can't open /dev/kfd or a crash, the Linux kernel is not exposing the compute interface to user space. If you are running a custom or bleeding-edge kernel, try booting into the stock or LTS kernel (linux-lts) to rule out a driver regression.

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Step 6 - Start the Server and Verify VRAM Usage

LM Studio:

lms server start

Ollama:

ollama serve

Then confirm the model is loaded into VRAM:

rocm-smi

Idle (no model loaded):

At idle the GPU draws minimal power (~9W), clocks are near-floor, and VRAM usage is low (~44%).

Under load (model + game running simultaneously):

Under combined load you should see VRAM usage climb significantly (71% in this example), GPU utilisation rise, and power draw increase accordingly (~147W). This confirms the model is resident in VRAM and inference is running on the GPU.