can-i-run-model

How much VRAM do you need to run Llama 3 locally?

A practical guide to GPU memory, system RAM, and quantization before downloading Llama models for local use.

Start with memory, not model hype

To run a Llama model locally, the first question is not only which GPU you own. The better question is how much free memory you have for model weights, context, and runtime overhead.

Most home users run quantized variants. An 8B model in Q4 is far more accessible than the same model at higher precision. Still, a browser, IDE, and background apps can reduce the memory that is actually available.

VRAM, RAM, and offload

When the whole model fits in VRAM, the experience is usually smoother. When part of the model has to offload into system RAM, the runtime may still work, but speed drops. If RAM also runs out, the operating system uses disk swap and responsiveness collapses.

That is why an 8GB GPU can be enough for many small models, while 12GB gives more breathing room for quantized 7B or 8B models. Larger models quickly make 16GB, 24GB, or more attractive.

Test in small steps

Download a small Q4 variant first. Open your resource monitor, run a short prompt, and confirm the GPU is actually being used. If time to first token is painful or RAM is pinned, reduce context length before changing models.

Treat requirements as a range rather than a magic number. Driver version, runtime, operating system, quantization, and context length all change the result.

How CanIRunAI helps

CanIRunAI uses GPU memory, system RAM, and CPU data to classify models into compatibility tiers. It is meant to help you avoid multi-gigabyte downloads that are unlikely to run well on your hardware.