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vLLM sleep mode needs an explicit procedure for returning a model to service.
vLLM sleep mode needs an explicit procedure for returning a model to service. A live API process does not establish that weights are restored and KV memory is allocated. Record the sleep level, wake sequence, and acceptance request so that partial readiness can be distinguished from a failure. Sleep is not a free pause that automatically preserves every conversation state or guarantees immediate continuation.
Establish what each level releases
The sleep-mode guide distinguishes offloading weights from discarding them along with other data. KV state should not be presumed preserved. Identify what must be restored from an external source and what may be recomputed. Keep this description specific to the tested release and scenario. A workflow updating model parameters between cycles has different restoration requirements from reusing an unchanged checkpoint, so one universal recovery recipe can be misleading. Name the authoritative weight source before exercising the lifecycle.
Check memory and support conditions
Consult the serve reference for enabling sleep and supported execution conditions. Do not automatically extend a CUDA example to every accelerator or allocator. Preserve runtime version, allocation method, and model revision. Acceptance must establish successful subsequent inference as well as resource release. A change in reported free memory observes one layer only; it does not prove that the engine can resume serving an existing queue. Keep memory observations separate from functional and orchestration-state checks in the test record.
Model the transition sequence
Define states for serving, sleeping, restored weights, prepared KV memory, and a passed acceptance request. This is a proposed validation model, not a claim about deployed Retrivium orchestration. Where partial wake tags are used, record each action and its observed state. Keep new inference closed in the test controller until required steps finish. A single health endpoint may report process liveness without establishing complete model restoration, so it cannot replace this sequence or its readiness criterion.
Validate functional recovery
After waking, repeat a saved short prompt and verify the response structure. Follow it with a representative request using the necessary context. Interpret is_sleeping according to the documentation, especially during partial wake, rather than guessing from the endpoint name. If weights are restored but inference still fails, inspect the next required stage instead of redownloading the checkpoint without evidence. Record a timeout and diagnostic messages for a stalled transition so that repetition does not become an unexplained infinite sleep-wake loop.
Preserve procedural limits
The acceptance artifact includes sleep level, wake tags, weight source, cache state, and the condition for reopening service. State which conversations require recomputation and what happens to in-flight requests in the tested workflow. Preserved weights do not establish preserved sessions. Measure recovery from a known state to functional readiness, separating transfer, allocation, and subsequent warmup. Repeat the entire cycle after an engine upgrade. A state diagram and transition log should let another operator reproduce the lifecycle without assuming that a running HTTP server already exposes a fully restored model.
Related reading
vLLM is running but not ready: health checks and the first request; Updating an LLM on a GPU: testing a new version with a small rollout.