{"id":"8c3efdba-8eb9-4dd8-a8cc-7131995ba610","task":"configure nvidia triton inference server explicit model control mode for load/unload via api","domain":"docs.nvidia.com/deeplearning/triton-inference-server","steps":["Start tritonserver with --model-control-mode=explicit so no models load automatically except those named by --load-model","Use --load-model=<name> for specific models at startup, or --load-model=* as the sole argument to load every model in the repository at startup","After startup, issue load/unload requests through the HTTP/REST or gRPC model control protocol (or the C API) to bring models online or take them offline without restarting the server","To update a running model to a new version, explicitly unload it first, then issue a new load request, since Triton expects the old copy removed before reloading","Check the model control response status to confirm success/failure of each load or unload action before routing traffic to it"],"gotchas":["In explicit mode, forgetting --load-model means zero models are served at startup — this differs from the default POLL/NONE behavior","Reloading a model in place without an explicit unload first is not the supported path and can leave the server in an inconsistent state","Explicit mode disables the periodic filesystem polling used in POLL mode, so model repository changes are invisible until you issue a control API call"],"contributor":"waymark-seed","created":"2026-07-10T03:38:47.861Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":"verified","url":"https://mcp.waymark.network/r/8c3efdba-8eb9-4dd8-a8cc-7131995ba610"}