{"id":"roboflow-roboflow-inference-server-gpu","name":"roboflow-inference-server-gpu","homepage":"https://hub.docker.com/r/roboflow/roboflow-inference-server-gpu","repo_url":"https://hub.docker.com/r/roboflow/roboflow-inference-server-gpu","category":"ai-ml","subcategories":[],"tags":["computer-vision","inference-server","gpu","self-hosted","roboflow","object-detection","segmentation","api-server"],"what_it_does":"roboflow-inference-server-gpu is a GPU-oriented inference server implementation intended to run Roboflow models and expose an inference API for computer-vision predictions.","use_cases":["Deploy Roboflow-trained computer-vision models for low-latency inference","Self-hosted object detection/segmentation inference pipelines on GPU hardware","Integrate vision inference into applications via HTTP requests to a local server"],"not_for":["Use as a managed/hosted SaaS API (it’s intended to be self-hosted infrastructure)","Teams needing a fully standardized enterprise API gateway experience (SDKs/openapi/webhooks not confirmed from provided info)"],"best_when":"You have GPU compute available and want to self-host vision inference for Roboflow models.","avoid_when":"You need strong, explicitly documented authentication/authorization, formal API contracts (OpenAPI), or guaranteed production SLOs from the provider (not verifiable from the provided data).","alternatives":["Roboflow hosted inference APIs (if available for your plan)","Other self-hostable inference servers (e.g., TensorRT/ONNX Runtime serving stacks)","Framework-native serving solutions (e.g., TorchServe, Triton Inference Server)"],"af_score":34.0,"security_score":39.8,"reliability_score":35.0,"package_type":"mcp_server","discovery_source":["docker_mcp"],"priority":"low","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-04-04T19:57:19.276917+00:00","interface":{"has_rest_api":true,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["Not specified in provided content; likely none or basic server auth if configured"],"oauth":false,"scopes":false,"notes":"The provided prompt contains only the package name; no explicit authentication mechanism, API key scheme, or scope model was available to verify. Assume minimal/unknown until documented by the repo README/config."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No pricing information was provided; as a server package, costs are likely infrastructure/GPU and engineering time rather than subscription."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":34.0,"security_score":39.8,"reliability_score":35.0,"mcp_server_quality":0.0,"documentation_accuracy":30.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":50.0,"rate_limit_clarity":20.0,"tls_enforcement":70.0,"auth_strength":25.0,"scope_granularity":20.0,"dependency_hygiene":50.0,"secret_handling":40.0,"security_notes":"TLS/auth behavior cannot be verified from the provided information. If the server is deployed behind a reverse proxy (common for inference servers), TLS may be enforced externally. Because auth scheme, scope granularity, and secret handling are not documented here, assume baseline risk and validate in the repo and runtime configuration.","uptime_documented":0.0,"version_stability":50.0,"breaking_changes_history":50.0,"error_recovery":40.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["No MCP interface indicated; agent integration likely requires direct HTTP calls.","Because auth/error/retry semantics are not provided in the prompt, an agent may need to discover them by running the server or reading the repository docs.","GPU inference servers often need careful payload sizing/latency handling; agents should avoid sending excessively large images without knowing limits."]}}