This paper explores the concept of zero-knowledge-friendly quantization for LLMs, which is crucial for making ZK-LLMs practical. The authors present a systematic study of ZK-friendly quantization, evaluating nine language models across a broad design space. Their results highlight the importance of activation precision and identify potential bottlenecks in large models.