Tag: Reproducibility

2 articles

• 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.

  07/15/2026 LLM Serving / Reproducibility / vLLM / CUDA / MLOps / Batch Invariance / Deterministic Inference / Seed Contract

• LLM Inference Reproducibility in Production: Controlling Output Drift with Batch Invariance, Fixed Seeds, and Configuration Fingerprints

  This article dissects why LLM inference can still produce drifting outputs even at zero temperature, and provides production-grade methods including fixed seeds, Batch Invariance, environment fingerprints, replay matrices, and tiered determinism gates to stabilize evaluations, audits, caching, and regression tests.

  07/12/2026 LLM Serving / Reproducibility / Batch Invariance / GPU Inference / MLOps