LLM Non-Determinism: What Providers Guarantee, and How to Build Around It

This post is based on sections 3--5 of "Understanding why deterministic output from LLMs is nearly impossible" by Shuveb Hussain at Unstract (Oct. 2025). The material has been rewritten and extended with practical code examples using Pydantic and Snowflake Cortex. Motivation When you build a pipeline that sends the same document through an LLM twice and expects the same structured output both times, you will eventually be surprised. Not because you've made a mistake, but because LLMs are fundamentally non-deterministic: the same prompt can produce different tokens across runs, even when you set temperature=0 . The root cause is that modern LLMs run on massively parallel GPU hardware, where floating-point arithmetic is not associative. The order in which thousands of parallel threads accumulate intermediate values is not guaranteed to be identical run-to-run, so the final token probability scores shift by tiny amounts. When two candidate tokens are close in probability, that tiny shift