How to Choose the Right GPU for Local LLMs (Without Wasting Money) TL;DR: Most people overspend on GPUs for local LLMs. If you match model size ↔ VRAM ↔ quantization , you can save hundreds (or thousands) and still get great results. Why this matters If you’re running local LLMs (Ollama, llama.cpp, vLLM, etc.), the biggest mistake I see is: Buying a GPU that’s too powerful (and too expensive) Or worse, buying one with not enough VRAM Both lead to frustration. This guide breaks down how to choose the right GPU for your actual workload — not just benchmarks. Step 1 — Understand what actually limits you For LLM inference, VRAM matters more than raw compute . Rough VRAM requirements Model Size Typical VRAM (quantized) Notes 7B 6–8GB Entry-level, very easy to run 13B 10–16GB Sweet spot for many users 34B 20–24GB High-end consumer GPUs 70B 40GB+ Usually cloud or multi-GPU If you remember one thing: VRAM determines what you can run. Compute determines how fast it runs.…