LLMs & Models5 min read
On-premise factory AI, arXiv preprint
Compression cost a factory assistant 13.7% of its answer quality
A new preprint argues that once a model is compressed and adapted with retrieval, parameter count stops predicting the quality of answers a factory-floor assistant gives. Its one case study loses 13.7 percent of judged quality to extraction and recovers two thirds of that.
2026-09-25