Running LLMs Locally: What You Need to Know
- Sam Wilson
- May 15, 2026
Table of Contents
Just two years ago, running large language models locally was the preserve of research teams with high-performance computers. Today, Llama 3 runs on a MacBook Pro. What changed — and what does it mean for businesses?
Quantisation Makes It Possible
The key is quantised models: by reducing weight precision from 32-bit float to 4-bit integer, a 7-billion-parameter model shrinks from ~28 GB to under 5 GB — with minimal quality loss.
Hardware Requirements in Practice
| Model Size | VRAM (GPU) | Use Case |
|---|---|---|
| 7B (Q4) | 6 GB | Single user, assistance |
| 13B (Q4) | 10 GB | Team deployment |
| 70B (Q4) | 48 GB | Enterprise, high quality |
Which Models Are Suitable?
For enterprise use, Mistral 7B (efficiency), Llama 3.1 8B (versatility) and Phi-3 Mini (resource-light) have proven themselves. SoverIQ Stack abstracts model selection and enables easy switching and A/B testing.
Running locally is no longer a compromise — it’s the sensible choice.