Large Language Gibbs uses LLM conditional distributions as transition operators for iterative variable resampling. This method enables probabilistically coherent structured inference by avoiding order-dependent biases and achieving a stationary distribution that balances local conditionals. It demonstrates practical efficacy in synthetic distributions, consistent reasoning, and Bayesian structure learning.