VRAM Is the New RAM — A Practical Guide to Running Large Language Models on Consumer GPUs

Your GPU has 8 GB of VRAM. The model you want to run needs 14 GB. What now? This is the most common wall people hit when running LLMs locally. Cloud APIs don't care about your hardware — local inference does. Understanding VRAM is the difference between smooth 40 tok/s responses and your system grinding to a halt. I've spent months optimizing local AI setups and building tools around Ollama. Here's everything I've learned about making large models fit on consumer hardware. Why VRAM Matters More Than You Think When you load a model into your GPU, every single parameter needs to live in VRAM during inference. A 7B parameter model in full FP16 precision needs roughly: 7 billion × 2 bytes = ~14 GB VRAM That's already more than most consumer GPUs. An RTX 4060 has 8 GB. An RTX 4070 has 12 GB. Even an RTX 4090 tops out at 24 GB. So how do people run 70B models on a single GPU? Quantization. Quantization Cheat Sheet Quantization reduces the precision of model weights. Instead of 16 bits per pa