AI GPU Buying Guide: Best GPU for Running Local LLMs

Selecting a GPU for local LLMs is complex because performance depends on multiple variables—not just raw power. Our guide simplifies this by letting you define your exact needs first. You begin by specifying the target models (e.g., Llama 3, Mistral), the level of quantization you plan to use (e.g., Q4_K_M for memory efficiency), and the minimum token generation speed (tokens/sec) you require.

The tool then processes this input against a comprehensive database of GPU specifications and community benchmarks. It doesn't just guess; it calculates estimated VRAM fit, predicts throughput based on your constraints, and presents a ranked table. This means you get actionable recommendations—like knowing that an RTX 3090 offers a better balance for 7B models than a similarly priced card with insufficient VRAM.

To get the most accurate and beneficial results from this guide, be as specific as possible with your inputs. Instead of just saying 'I want to run local LLMs,' define your use case.

• Define Your Baseline: Specify a known model, such as Llama 3 8B Q4_K_M. This gives the tool an immediate benchmark to work from.
• Set a Realistic Speed Goal: If you need conversational speed, aim for at least 15+ tokens/sec. Adjusting this minimum speed will filter out unsuitable hardware immediately.
• Test Different Quantizations: Run the tool multiple times—once with Q8 and once with Q4—to see how much VRAM savings translate into performance gains versus quality loss.