Just got a local AI model to run on my 4-year-old laptop and I'm shocked

I've been trying to run local LLMs on my old Dell for months and it was always way too slow. Finally tried a quantized 7B model with 4-bit compression instead of the full version. It went from taking 30 seconds per response down to about 5 seconds. I used LM Studio with the llama.cpp backend to make it work. Had to tweak the context window down to 2048 tokens but the output is still solid. Has anyone else tried running these smaller quantized models for basic coding help or writing?

2 comments

2 Comments

barbara_taylor8311d ago

Honestly, I think you're just wasting your time with these tiny models. A 7B quantized down to 4 bits is basically a glorified autocomplete, it can't handle any complex logic or code that isn't already memorized. I tried one for basic Python help and it kept suggesting broken syntax and hallucinating library functions that don't exist. You're better off just using the cloud or paying for a proper API, the speed gain isn't worth the drop in quality.

the_fiona11d agoMost Upvoted

My 4GB RAM laptop runs a 3B model at 6 bits per weight and it handles my day to day stuff just fine... I was trying to make a simple compound interest calculator in Python and it gave me working code on the first try. The key is matching the model size to what you actually need instead of going for the biggest one you can cram in. Barbara you're right that the tiny ones hallucinate libraries, but thats more about picking a model that was trained well rather than just any random one. I use phi-3-mini for basic scripting and it rarely messes up standard Python stuff.