Running local LLMs on consumer AMD GPUs often comes with two pain points: configuring backend overrides (HSA_OVERRIDE_GFX_VERSION) and figuring out whether Vulkan or HIP/ROCm gives better token throughput on your setup.

I created two open-source tools to solve this:

1. ROCmFix Repo: github.com/xanpavle/rocmfix

Zero-dependency Python script runnable via single-line terminal command.

Detects GPU PCI IDs directly (Windows Registry / Linux lspci) to map the correct HSA_OVERRIDE_GFX_VERSION.

Automatically updates shell profiles (PowerShell, CMD, Bash, Zsh, Fish) and includes an undo command and driver diagnostic runner (rocmfix doctor).

2. InferBench Repo: github.com/xanpavle/inferbench

Automated benchmarking tool for local LLM servers (Ollama, LM Studio).

Rigorously compares Vulkan vs HIP/ROCm performance with warm-up passes, median of N runs, and cold-start VRAM unloading between test cycles.

Both projects are MIT-licensed. Check them out and let me know your benchmarking results!