> ## Documentation Index > Fetch the complete documentation index at: https://infercrane.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Provider-native serverless > Preserve a stable endpoint while a qualified provider owns workers and scale-to-zero. # Provider-native serverless InferCrane delegates worker allocation, queueing, scale-up, idle scale-down, and GPU scheduling to a registered provider-native backend. InferCrane owns the logical deployment, durable endpoint operation, immutable model identity, routing metadata, telemetry, deletion, and explanations. Animated provider-native serverless lifecycle from zero workers through cold allocation, a warm request, and provider idle scale-down to zero. Backends implement one lifecycle contract for endpoint creation, observation, deletion, inventory, health, and an OpenAI-compatible endpoint URL. InferCrane does not contain a GPU scheduler or a provider switch in its durable serverless workflow. ## RunPod implementation RunPod Serverless is the first qualified backend. Create or select a template using RunPod's maintained vLLM worker. The template must set: * `MODEL_NAME` to the exact Hugging Face repository, such as `Qwen/Qwen3-8B`. * `MODEL_REVISION` to the immutable Hugging Face commit resolved for the deployment. `main` and `master` are rejected. * `RAW_OPENAI_OUTPUT=1` so streaming remains OpenAI-compatible SSE. * `ENABLE_AUTO_TOOL_CHOICE=true` and a model-compatible `TOOL_CALL_PARSER`. For `Qwen/Qwen3-8B`, RunPod recommends `TOOL_CALL_PARSER=hermes`. Configure the control plane, keeping the RunPod credential separate from InferCrane client credentials: ```bash theme={"theme":{"light":"github-light-default","dark":"vesper"}} export RUNPOD_API_KEY='...' export INFERCRANE_RUNPOD_SERVERLESS_TEMPLATE_ID='...' infercrane doctor --serverless ``` The template ID is intentionally explicit. InferCrane does not build or own a custom inference image, and it refuses to create an endpoint when the template does not match the requested immutable model artifact. InferCrane also constrains RunPod worker placement to hosts with a CUDA 13-capable driver. The maintained `worker-v1-vllm` image requires CUDA 13 or newer; without that provider constraint a worker can be placed successfully and then exit before model initialization begins. ## Deploy ```bash theme={"theme":{"light":"github-light-default","dark":"vesper"}} infercrane plan Qwen/Qwen3-8B --compute serverless --cloud runpod --gpu L40S --max 4 infercrane deploy Qwen/Qwen3-8B --compute serverless --cloud runpod --gpu L40S --max 4 --wait ``` Serverless deployments always use zero minimum workers. The logical InferCrane endpoint remains stable while the provider scales workers from zero on the first request, back to zero after its idle timeout, and from zero again on later requests. InferCrane does not poll the inference URL for health because doing so would create or retain warm workers. Inference requests continue to use the InferCrane credential and logical model name. The gateway replaces that credential with the registered upstream credential only for the upstream request. It forwards streaming incrementally and propagates client cancellation through the request context. ## Delete and recovery Deletion first withdraws the logical route, then deletes the provider endpoint, confirms that the endpoint is absent from provider inventory, removes persisted target capacity, and finally removes the logical deployment. Every step is a durable operation and can resume after a control-plane restart. Endpoint creation is replay-safe. Before creating capacity, InferCrane lists endpoints for the deterministic deployment/revision key. A retry adopts the one exact matching endpoint; multiple matches or immutable-spec mismatches fail visibly instead of creating another billable resource. ## Current limitations * The first backend supports one configured immutable vLLM template per control-plane process. * RunPod is currently the only executable native Serverless adapter. Real cold/warm, streaming, cancellation, scale-to-zero, and billing acceptance remains a final manual gate. * InferCrane records request timing and token metadata but does not record prompts or generated content by default. See [cold-start intelligence](/features/cold-starts) for the exact classification evidence and unavailable provider timing boundaries.