OpenVINO™ Model Server is a high-performance inference serving system designed to host and serve machine learning models that have been optimized with the OpenVINO toolkit. It’s implemented in C++ for scalability and efficiency, making it suitable for both edge and cloud deployments where inference workloads must be reliable and high throughput. The server exposes model inference via standard network protocols like REST and gRPC, allowing any client that speaks those protocols to request predictions remotely, abstracting away the complexity of where and how the model runs. It supports model deployment in diverse environments including Docker, bare-metal machines, and Kubernetes clusters, and is especially useful in microservices architectures where AI services need to scale independently. The system supports a wide range of model sources, letting you host models from local storage, remote object storage, or even pull from model hubs.
Features
- Serve optimized OpenVINO models over REST and gRPC
- Scales horizontally and vertically for production workloads
- Deploy across Docker, bare metal, and Kubernetes environments
- Support for models stored locally or in remote object storage
- Compatible with standard serving APIs (TensorFlow/KServe)
- Tools and demos for embeddings, genAI, and real-time use cases