Optimizing for Throughput

As described in the section on the latency-specific considerations, one of the possible use cases is delivering every single request at the minimal delay. Throughput, on the other hand, is about inference scenarios in which potentially large number of inference requests are served simultaneously to improve the device utilization.

The associated increase in latency is not linearly dependent on the number of requests executed in parallel. A trade-off between overall throughput and serial performance of individual requests can be achieved with the right performance configuration of OpenVINO.

Basic and Advanced Ways of Leveraging Throughput

There are two ways of leveraging throughput with individual devices:

In both cases, the application should be designed to execute multiple inference requests in parallel, as described in the following section.

Throughput-Oriented Application Design

In general, most throughput-oriented inference applications should:

  • Expose substantial amounts of input parallelism (e.g. process multiple video- or audio- sources, text documents, etc).

  • Decompose the data flow into a collection of concurrent inference requests that are aggressively scheduled to be executed in parallel:

    • Setup the configuration for the device (for example, as parameters of the ov::Core::compile_model) via either previously introduced low-level explicit options or OpenVINO performance hints (preferable):

      auto compiled_model = core.compile_model(model, "GPU",
      compiled_model = core.compile_model(model, "GPU", config)
    • Query the ov::optimal_number_of_infer_requests from the ov::CompiledModel (resulted from a compilation of the model for the device) to create the number of the requests required to saturate the device.

  • Use the Async API with callbacks, to avoid any dependency on the completion order of the requests and possible device starvation, as explained in the common-optimizations section.

Multi-Device Execution

OpenVINO offers the automatic, scalable multi-device inference mode, which is a simple application-transparent way to improve throughput. There is no need to re-architecture existing applications for any explicit multi-device support: no explicit network loading to each device, no separate per-device queues, no additional logic to balance inference requests between devices, etc. For the application using it, multi-device is like any other device, as it manages all processes internally. Just like with other throughput-oriented scenarios, there are several major pre-requisites for optimal multi-device performance:

  • Using the Asynchronous API and callbacks in particular.

  • Providing the multi-device (and hence the underlying devices) with enough data to crunch. As the inference requests are naturally independent data pieces, the multi-device performs load-balancing at the “requests” (outermost) level to minimize the scheduling overhead.

Keep in mind that the resulting performance is usually a fraction of the “ideal” (plain sum) value, when the devices compete for certain resources such as the memory-bandwidth, which is shared between CPU and iGPU.


While the legacy approach of optimizing the parameters of each device separately works, the OpenVINO performance hints allow configuring all devices (that are part of the specific multi-device configuration) at once.