How do you ensure consistency across distributed LLM inference nodes?

Consistency is ensured through model versioning, deterministic decoding settings, and synchronized deployment pipelines.

In distributed inference, inconsistencies arise from model version mismatch, temperature settings, or hardware differences. Production systems enforce strict version pinning, deterministic decoding (temperature=0, fixed seed where applicable), and rollout strategies like canary deployments to maintain consistency across nodes.