When local models may be less practical than paid AI
A setup with a Mac Mini M4 with 16GB of memory and a PC with an RTX 4070 Ti with 12GB of VRAM was tested with several , including Hermes. The practical result is narrow: 8B to 12B parameter models can handle small jobs such as collecting and summarizing morning news, sorting a downloads folder, casual chat, simple one-shot code, and checking a small built-in .
Free commercial AI services can already do those jobs easily. The harder uses are where the local setup falls short: , reliable tool or skill use, and back-and-forth code debugging are described as not practical on this class of model.
Buying stronger hardware is also a cost problem. A single GPU with 32GB of VRAM can cost about the same as 100 months of a 20 dollar monthly , which makes the hardware upgrade hard to justify for many users.
Key points
- The tested setup used a Mac Mini M4 with 16GB of memory and an RTX 4070 Ti PC with 12GB of VRAM.
- Hermes and similar worked for small tasks like summaries, file sorting, chat, and simple code.
- 8B to 12B parameter models struggled with , tool use, and repeated code debugging.
- A 32GB VRAM GPU may cost about as much as 100 months of a 20 dollar .
- For , match the model to the job instead of forcing every task onto local AI.