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Local LLM user weighs AMD GPU upgrade for llama.cpp performance

By PulseAugur Editorial · [1 sources] ·

A user is seeking advice on upgrading their GPU setup for running large language models locally using llama.cpp. They are considering replacing two RTX 2060 12GB cards (totaling 24GB VRAM) with an RX 6800 16GB and an RX 6800 XT 16GB (totaling 32GB VRAM). The user is concerned about the performance implications of this mixed AMD setup in llama.cpp, specifically regarding ROCm or Vulkan support and overall token generation speed compared to their current CUDA setup. AI

IMPACT Potential hardware choices for optimizing local LLM inference performance.

RANK_REASON User query about hardware for running LLMs locally.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Local LLM user weighs AMD GPU upgrade for llama.cpp performance

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User query about hardware for running LLMs locally.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 (CA) · /u/BarberIcy366 ·

    Is RX 6800 + 6800 XT a sensible upgrade from 2x RTX 2060 OC 12GB for llama.cpp?

    <!-- SC_OFF --><div class="md"><p>I’m currently running llama.cpp on two RTX 2060 12GB cards, so 24GB total VRAM. With Qwen3.8 27B IQ4_XS at 131k context I’m getting around 45 tok/s, which is actually pretty good for this setup.</p> <p>I found a deal on an RX 6800 16GB and an RX …

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