[michaelt]

Take a look at the hardware requirements at https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#...

A 'LoRA' is a memory-efficient type of fine tuning that only tunes a small fraction of the LLM's parameters. And 'quantisation' reduces an LLM to, say, 4 bits per parameter. So it's feasible to fine-tune a 7B parameter model at home.

Anything bigger than 7B parameters and you'll want to look at renting GPUs on a platform like Runpod. In the current market, there are used 4090s selling on ebay right now for $2100 while runpod will rent you a 4090 for $0.34/hr - you do the math.

It's certainly possible to scale model training to span multiple nodes, but generally scaling through bigger GPUs and more GPUs per machine is easier.