AMD RX 9060 XT LoRA Training Guide Uses Native Linux ROCm
Follow the AMD fine‑tuning guide to train LLMs on Strix Halo and Ryzen AI Max 395 using RoCM.
Follow the guide to fine‑tune your own LLM on AMD hardware and compare performance with NVIDIA setups.
Summary
A newly published guide details how to train a LoRA model on an AMD RX 9060 XT graphics card using native Linux ROCm, bypassing the problematic WSL2 DXG bridge that often causes kernel dispatch failures. The author explains that the setup requires a 16 GB GDDR6 GPU, a Ryzen 5 5600G CPU, 32 GB of RAM, and Kubuntu 24.04.4 running kernel 6.17.0‑23. The guide stresses that ROCm must be installed from the official .deb package, and that the user must be added to the render and video groups so that the amdgpu module can load correctly.
The instructions recommend using the cupertinomiranda/ai‑toolkit‑amd‑rocm‑support fork of the AI toolkit, compiling bitsandbytes from source with the gfx1200 flag, and avoiding PyTorch nightly wheels to maintain stability. It also covers how to download SDXL fp16 models, create symbolic links for missing files, and monitor GPU usage with rocm‑smi. By following these steps, the author demonstrates that native Linux training can complete a single step in seconds, whereas the same task on WSL2 can take several minutes, highlighting a significant performance advantage.
The guide’s performance benchmarks show that the native ROCm environment delivers faster training times and lower latency, making it a practical choice for researchers and hobbyists working with RDNA4 hardware. The detailed, step‑by‑step approach is designed to help users avoid trial‑and‑error and quickly achieve efficient LoRA training on AMD GPUs.
Key changes
- Tutorial for fine‑tuning LLMs on AMD Strix Halo and Ryzen AI Max 395
- Supports Linux and pure Windows (no WSL)
- Enables full SFT and LoRA training
- Utilizes RoCM for AMD GPU acceleration
- Provides step‑by‑step scripts and example configurations