AMD RX580 runs LLMs at home: beating ROCm and Ollama to get real GPU inference

Running an LLM on an old AMD RX580 took more than one lucky command. It turned into a full engineering investigation. The author tried to get proper GPU inference through ROCm and Ollama in Kubernetes. Instead of stable generation, he got false signs of success, out-of-memory crashes, and, at times, meaningless output.

Symptoms and traps

At first, everything looked almost functional. The system detected the graphics card, the containers started, and VRAM filled up, so the GPU appeared to be in use. That was the trap: occupied memory does not mean the GPU is computing correctly. The main problem surfaced during actual inference. Requests failed with hipMemGetInfo errors or produced strange output that looked like the model was working but carried no real meaning.

The GPU was detected, VRAM was allocated, containers were starting —

but inference was failing with hipMemGetInfo errors.

This case shows a common mistake in local LLM deployments: trusting the infrastructure's "signs of life." Kubernetes can start the container, Ollama can see the model, and the GPU can take several gigabytes, and none of that confirms that the ROCm stack is executing matrix operations correctly. On older cards like the RX580, you need to check the actual compute path, not just whether the device is available, because the failure can hide below the application layer.

How the cause was found

The author narrowed down the root cause by diagnosing the compute path, not by reinstalling packages yet again. He compared signs of activity across different layers of the system and separated cosmetic successes from real inference. Vulkan unexpectedly became the key tool. It showed whether the GPU could handle compute workloads reliably at all, and it made clear that the problem was not limited to Ollama or the container configuration.

In effect, the investigation moved from symptoms to testable hypotheses. Instead of guessing from logs, the author ruled out false explanations one by one and built a minimal working configuration, checking each layer separately: from the container and runtime to the drivers and the model itself. That order matters because it shows where "the infrastructure is up" ends and the real compute pipeline begins.

The breakdown went step by step:

• Checking real GPU compute, not just VRAM usage
• Comparing ROCm and Vulkan behavior
• Ruling out container and orchestration issues
• Finding compatible kernel and ROCm versions
• Checking the quality of the model's output

This approach matters because garbage output is a diagnostic signal too. If the model responds but generates nonsense, the failure may lie not in loading the weights but in faulty computation, driver incompatibility, or a partially working backend that only looks alive on the surface. These half-working states often eat more time than a complete failure, because they masquerade as random application bugs.

A working setup on the RX580

The experiment did not end with a "magic setting." It ended with a specific combination of versions and components that gets stable results out of the old RX580. The author writes that particular versions of ROCm and the Linux kernel worked, and once the conflicts were resolved, inference stopped crashing and began producing normal text. That is an important takeaway for anyone running local models on older AMD graphics: success depends less on nominal hardware support and more on an exact match between the driver, system, and runtime layers.

The practical result is convincing: the RX580 reached about 42 tokens per second. For a previous-generation home card, that is a usable working mode, not just a demo. It is enough to test local assistants, RAG prototypes, and personal inference services without a mandatory upgrade to a current NVIDIA stack. But the main lesson is the method, not the speed figure: a GPU that "seems to work" is not enough. You need to verify compute stability, output correctness, and reproducibility.

What this means

The RX580 story shows that local LLM inference on older AMD hardware is possible, but it demands disciplined diagnostics. For developers, it offers a clear guideline: don't mistake occupied VRAM for a working model, check the entire stack from kernel to runtime, and treat strange output as a real error rather than a minor glitch. For home labs, it is a near-ready checklist for avoiding days lost to false signs of success.