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# Building a Poverty-Spec AI Cluster: Repurposing RX 580s for Local LLMs

**[SitePoint](https://daily.dev/sources/sitepoint)** · 23 min read · 0 upvotes · 0 comments

## Summary

A detailed guide to building a budget multi-GPU LLM inference rig using four AMD RX 580 8GB cards sourced from crypto-mining liquidation sales for $40–$60 each. The total build costs under $500 and runs quantized GGUF models via llama.cpp. Covers hardware selection and gotchas (ex-mining cards, PSU sizing, BIOS settings), two software paths—ROCm/HIP with the HSA_OVERRIDE_GFX_VERSION=8.0.3 workaround for unsupported Polaris GPUs, and the more reliable Mesa Vulkan fallback—plus compiling llama.cpp for AMD multi-GPU inference, layer splitting with --tensor-split, realistic performance benchmarks (8–15 t/s for 7B models, 2–5 t/s for 70B across four cards), undervolting tips, and an honest assessment of limitations including PCIe x1 bandwidth bottlenecks and driver fragility.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.sitepoint.com/poverty-spec-ai-cluster-rx-580-local-llm/>

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---

Tags: [#llama-cpp](https://daily.dev/tags/llama-cpp), [#local-ai](https://daily.dev/tags/local-ai), [#rocm](https://daily.dev/tags/rocm), [#self-hosting](https://daily.dev/tags/self-hosting)

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