Local LLM on a 2017 Graphics Card: AMD RX 580 + Vulkan + Ollama
The 2017 AMD RX 580 graphics card can run modern language models thanks to Vulkan. No need to deal with ROCm complexity — use straightforward Vulkan and get 15–
Local AI has become a reality even for old hardware. AMD RX 580, a graphics card from 2017, is capable of running modern language models on a local computer at a speed of 15–35 tokens per second. Not the cloud, not an API, not subscriptions — pure local AI on a machine that was forgotten in a drawer.
Vulkan instead of ROCm ROCm — AMD's official support for GPU
acceleration — often creates problems on Fedora: complex installation, version incompatibilities, gaps in documentation. Vulkan offers an alternative: it's a standard graphics API available everywhere, working without pain. Ollama supports Vulkan, and this changes the game — no more need to wrestle with ROCm. A speed of 15–35 tokens per second is quite realistic for a 2017 graphics card. This doesn't compete with modern GPUs like the RTX 4090, but is sufficient for local use: running Llama 3.1, DeepSeek, Qwen 3.5, experimenting with models, integrating into your own applications without cloud APIs.
How to set up a local AI stack
The process is surprisingly simple: Install Ollama — a minimalist model launcher for any OS Run Open WebUI — a web interface for interacting with models Connect n8n — a platform for automation and complex workflows Load any open model — Llama 3.1, DeepSeek V2, Qwen 3.5 Vulkan is automatically used by Ollama if the graphics card is compatible. On Fedora, everything works out of the box — no additional configuration needed.
Real performance
On AMD RX 580 you will get: Llama 3.1 70B with quantization: ~20 tokens per second DeepSeek V2: ~18 tokens per second * Qwen 3.5 32B: ~32 tokens per second This is sufficient for interactive use — you won't get an instant answer like in ChatGPT, but a fully ready result will arrive in 5–15 seconds. For batch processing hundreds of texts, speed doesn't matter at all. Plus: complete privacy. All data remains on your machine, no requests to OpenAI, Anthropic, or other cloud services.
What this means Local AI is no longer a privilege of owners of premium hardware.
An old graphics card lying unused suddenly becomes a useful tool for development and experiments. This opens the door to private AI, experiments independent of cloud services, and integration of models directly into your own projects.