Can huge AI models still work well when compressed to Q1 or Q2?
The item asks whether very large AI models, roughly 100B to 250B in size, are still useful when compressed to very low settings called Q1 or Q2. It lists examples such as , Qwen3-235B-A22B, MiniMax-M2.X, GLM-4.5-Air, Mistral models, and Llama models.
Some people already use Q3 when their machines cannot handle Q4, including cases where MiniMax-M2 at Q4 is too tight for available hardware. The idea is that small and medium models may lose too much quality at Q1 or Q2, but very large models might still have enough capacity to remain useful after heavy .
The main question is whether Q1 or Q2 versions of big models are good enough for , writing, and normal chat. The concerns include looping, repetition, and other behavior that can make the model less reliable.
Key points
- The focus is on 100B to 250B AI models compressed to Q1 or Q2.
- Some users already drop to Q3 when Q4 does not fit their hardware well.
- The open question is whether huge models survive heavy better than small or medium models.
- The practical use cases are , writing, and chat.
- Looping and repetition are the main quality risks being checked.