Performance tuning


This document gives an overview of various parameters that can be configured to achieve maximum performance efficiency.

Adjusting the number of Inference Engine streams in CPU and GPU target devices

OpenVINO Model Server can be tuned to a single client use case or a high concurrency. It is done via setting the number of execution streams. They split the available resources to perform parallel execution of multiple requests. It is particularly efficient for models which can not consume effectively all CPU cores or for CPUs with high number of cores.

By default, OpenVINO Model Server sets the value CPU_THROUGHPUT_AUTO. It calculates the number of streams based on number of available vCPUs. It gives a compromise between the single client scenario and the high concurrency.

If this default configuration is not suitable, adjust it with the parameter CPU_THROUGHPUT_STREAMS defined as part of the device plugin configuration.

In a scenario where the number of parallel connections is close to 1, set the following parameter:

–plugin_config {“CPU_THROUGHPUT_STREAMS”: “1”}’`

When the number of concurrent requests is higher, increase the number of streams. Make sure, however, the number of streams is lower then the average volume of concurrent inference operations. Otherwise the server might not be fully utilized. Number of streams should not exceed the number of CPU cores.

For example with ~50 clients sending the requests to the server with 48 cores, set the number of streams to 24:

–plugin_config {“CPU_THROUGHPUT_STREAMS”: “24”}’`

Input data in REST API calls

While using REST API, you can adjust the data format to optimize the communication and deserialization from json format.

While sending the input data for inference execution, try to adjust the numerical data type to reduce the message size.

  • reduce the numbers precisions in the json message with a command similar to np.round(imgs.astype(np.float),decimals=2).

  • use binary data format encoded with base64 - sending compressed data will greatly reduce the traffic and speed up the communication.

  • with binary input format it is the most efficient to send the images with the resolution of the configured model. It will avoid image resizing on the server to fit the model.


OpenVINO Model Server can be scaled vertically by adding more resources or horizontally by adding more instances of the service on multiple hosts.

While hosting multiple instances of OVMS with contrained CPU resources, it is optimal to ensure CPU affinity for the containers. It can be arranged via CPU manager for Kuburnetes.

An equivalent in the docker, would be starting the containers with the option --cpuset-cpus instead of --cpus.

In case of using CPU plugin to run the inference, it might be also beneficial to tune the configuration parameters like :




Specifies the number of threads that CPU plugin should use for inference.


Binds inference threads to CPU cores.


Specifies number of CPU “execution” streams for the throughput mode

NOTE: For additional information about all parameters read OpenVINO supported plugins.

  • Example :

  1. While passing the plugin configuration, omit the KEY_ phase.

  2. Following docker command will set KEY_CPU_THROUGHPUT_STREAMS parameter to a value KEY_CPU_THROUGHPUT_NUMA :

docker run --rm -d --cpuset-cpus 0,1,2,3 -v <model_path>:/opt/model -p 9001:9001 openvino/model_server:latest\
--model_path /opt/model --model_name my_model --port 9001 \
--plugin_config '{"CPU_THROUGHPUT_STREAMS": "1"}'

Tuning Model Server configuration parameters

OpenVINO Model Server in C++ implementation is using scalable multithreaded gRPC and REST interface, however in some hardware configuration it might become a bottleneck for high performance backend with OpenVINO.

  • To increase the throughput, a parameter --grps_workers is introduced which increases the number of gRPC server instances. In most cases the default value of 1 will be sufficient. In case of particularly heavy load and many parallel connections, higher value might increase the transfer rate.

  • Another parameter impacting the performance is nireq. It defines the size of the model queue for inference execution. It should be at least as big as the number of assigned OpenVINO streams or expected parallel clients (grpc_wokers >= nireq).

  • Parameter file_system_poll_wait_seconds defines how often the model server will be checking if new model version gets created in the model repository. The default value is 1 second which ensures prompt response to creating new model version. In some cases it might be recommended to reduce the polling frequency or even disable it. For example with cloud storage, it could cause a cost for API calls to the storage cloud provider. Detecting new versions can be disabled with a value 0.

Plugin configuration

Depending on the device employed to run the inference operation, you can tune the execution behaviour with a set of parameters. Each device is handled by its OpenVINO plugin.

Note: For additional information, read supported configuration parameters for all plugins.

Model’s plugin configuration is a dictionary of param:value pairs passed to OpenVINO Plugin on network load. It can be set with plugin_config parameter.

Following docker command sets a parameter KEY_CPU_THROUGHPUT_STREAMS to a value 32 and KEY_CPU_BIND_THREAD to NUMA.

docker run --rm -d -v <model_path>:/opt/model -p 9001:9001 openvino/model_server:latest \
--model_path /opt/model --model_name my_model --port 9001 --grpc_workers 8  --nireq 32 \
--plugin_config '{"CPU_THROUGHPUT_STREAMS": "32", "CPU_BIND_THREAD": "NUMA"}'