How much VRAM do I need to run AI models locally?

24 GB VRAM (e.g. RTX 4090) runs most 7B–13B models at full quality and 70B models at Q4 quantization. 16 GB (RTX 4080) handles 7B–13B comfortably. For 70B+ models at high quality, consider 48 GB (RTX A6000) or multi-GPU setups.

Is it cheaper to build a local AI PC or rent cloud GPUs?

At moderate usage (160 hours/month), a local AI build typically pays for itself in 3–8 months compared to cloud GPU rentals from providers like Lambda, RunPod, or AWS. The break-even depends on your GPU choice and usage intensity.

What is quantization in AI models?

Quantization reduces model precision (e.g. from FP16 to Q4) to use less VRAM with minimal quality loss. A 70B model at FP16 needs ~140 GB VRAM, but at Q4 only ~40 GB. Most users run Q4 or Q6. The quality difference is barely noticeable for chat and coding tasks.

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Data Accuracy: Performance figures are estimates based on community benchmarks and may vary by configuration, driver version, and software. Prices are approximate US retail as of March 2026 and may vary by retailer and region. VRAM requirements are calculated from model parameters with overhead estimates. Always verify specifications with manufacturer documentation before purchasing.