LLM Quantization

Compression sounds simple until the model starts forgetting how to think. A deployment team takes a large language model, squeezes its weights into lower precision, saves memory, improves serving economics, and expects the model to behave like a slightly thinner version of itself. Then INT4 arrives with a polite smile and removes just enough reasoning ability to make the business case awkward. The model still answers. It still looks fluent. It just becomes less reliable exactly where the product needed it to stay sharp. ...

TL;DR for operators If your deployment plan depends on squeezing a language model into cheap inference hardware, this paper is worth reading because it changes the timing of the quantization problem. Most quantization work asks: “How do we repair a model after training so it survives 4-bit inference?” Outlier-Safe Pre-Training asks a more irritating question: “Why did we train a quantization-hostile model in the first place?”1 ...