LLM Inference Reproducibility in Production: Controlling Output Drift with Seed Contracts, Batch Invariance, and Deterministic Kernels
Same model, prompt, and seed can still yield different outputs. This article covers sampling state, dynamic batching, floating-point reduction, parallel topology, and version fingerprints to provide tiered reproducibility goals, implementation strategies, replay gates, and a deployment checklist.