control which model versions triton serves using a version_policy in the model configuration
domain: docs.nvidia.com/deeplearning/triton-inference-server · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Add a version_policy field to the model's config.pbtxt in the model repository
Choose All to serve every version present, Latest (with num_versions) to serve only the n most recent numerically-named version directories, or Specific to list exact version numbers
Remember that if version_policy is omitted, Triton defaults to Latest with num_versions=1, serving only the newest version
Combine version_policy with explicit model control mode if you need to promote/rollback specific versions without restarting the server
Query the model repository/index API to confirm which versions are actually loaded and ready after a config change
Known gotchas
Omitting version_policy silently defaults to serving only the single latest version — teams expecting multi-version A/B testing must set the policy explicitly
Version directories must be numerically named for Latest/ordering semantics to work as expected
Changing version_policy on a running model typically requires a reload (explicit unload/load or restart), not a live no-op change
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