Model Server hosts models and makes them accessible to software components over standard network protocols: a client sends a request to the model server, which performs model inference and sends a response back to the client. Model Server offers many advantages for efficient model deployment:

• Remote inference enables using lightweight clients with only the necessary functions to perform API calls to edge or cloud deployments.
• Applications are independent of the model framework, hardware device, and infrastructure.
• Client applications in any programming language that supports REST or gRPC calls can be used to run inference remotely on the model server.
• Clients require fewer updates since client libraries change very rarely.
• Model topology and weights are not exposed directly to client applications, making it easier to control access to the model.
• Ideal architecture for microservices-based applications and deployments in cloud environments – including Kubernetes and OpenShift clusters.
• Efficient resource utilization with horizontal and vertical inference scaling.

OpenVINO™ Model Server (OVMS) is a high-performance system for serving models. Implemented in C++ for scalability and optimized for deployment on Intel architectures, the model server uses the same architecture and API as TensorFlow Serving and KServe while applying OpenVINO for inference execution. Inference service is provided via gRPC or REST API, making deploying new algorithms and AI experiments easy.

The models used by the server need to be stored locally or hosted remotely by object storage services. For more details, refer to Preparing Model Repository documentation. Model server works inside Docker containers, on Bare Metal, and in Kubernetes environment. Start using OpenVINO Model Server with a fast-forward serving example from the Quickstart guide or explore Model Server features.

Read release notes to find out what’s new.

• [NEW] Efficient Text Generation via OpenAI API - preview
• Python code execution
• gRPC streaming
• MediaPipe graphs serving
• Model management - including model versioning and model updates in runtime
• Dynamic model inputs
• Directed Acyclic Graph Scheduler along with custom nodes in DAG pipelines
• Metrics - metrics compatible with Prometheus standard
• Support for multiple frameworks, such as TensorFlow, PaddlePaddle and ONNX
• Support for AI accelerators

Note: OVMS has been tested on RedHat, and Ubuntu. The latest publicly released docker images are based on Ubuntu and UBI. They are stored in:

• Dockerhub
• RedHat Ecosystem Catalog

A demonstration on how to use OpenVINO Model Server can be found in our quick-start guide. For more information on using Model Server in various scenarios you can check the following guides:

• Model repository configuration

• Deployment options

• Performance tuning

• Directed Acyclic Graph Scheduler

• Custom nodes development

• Serving stateful models

• Deploy using a Kubernetes Helm Chart

• Deployment using Kubernetes Operator

• Using binary input data

• OpenVINO™

• TensorFlow Serving

• gRPC

• RESTful API

• Benchmarking results

• Speed and Scale AI Inference Operations Across Multiple Architectures - webinar recording

• What is new in OpenVINO Model Server C++

• Capital Health Improves Stroke Care with AI - use case example

If you have a question, a feature request, or a bug report, feel free to submit a Github issue.

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