Performance Benchmarks#
This page presents benchmark results for the Intel® Distribution of OpenVINO™ toolkit and OpenVINO Model Server, for a representative selection of public neural networks and Intel® devices. The results may help you decide which hardware to use in your applications or plan AI workload for the hardware you have already implemented in your solutions. Click the buttons below to see the chosen benchmark data.
Key performance indicators and workload parameters
For Vision and NLP Models this measures the number of inferences delivered within a latency threshold (for example, number of Frames Per Second - FPS). For GenAI (or Large Language Models) this measures the token rate after the first token aka. 2nd token throughput rate which is presented as tokens/sec. Please click on the “Workload Parameters” tab to learn more about input/output token lengths, etc.
For Vision and NLP models this measures the synchronous execution of inference requests and is reported in milliseconds. Each inference request (for example: preprocess, infer, postprocess) is allowed to complete before the next one starts. This performance metric is relevant in usage scenarios where a single image input needs to be acted upon as soon as possible. An example would be the healthcare sector where medical personnel only request analysis of a single ultra sound scanning image or in real-time or near real-time applications such as an industrial robot’s response to actions in its environment or obstacle avoidance for autonomous vehicles. For Transformer models like Stable-Diffusion this measures the time it takes to convert the prompt or input text into a finished image. It is presented in seconds.
The workload parameters affect the performance results of the different models we use for benchmarking. Image processing models have different image size definitions and the Natural Language Processing models have different max token list lengths. All these can be found in detail in the FAQ section. All models are executed using a batch size of 1. Below are the parameters for the GenAI models we display.
Input tokens: 1024,
Output tokens: 128,
number of beams: 1
For text to image:
iteration steps: 20,
image size (HxW): 256 x 256,
input token length: 1024 (the tokens for GenAI models are in English).
Platforms, Configurations, Methodology
To see the methodology used to obtain the numbers and learn how to test performance yourself, see the guide on getting performance numbers.
For a listing of all platforms and configurations used for testing, refer to the following:
Disclaimers
Intel® Distribution of OpenVINO™ toolkit performance results are based on release 2024.6, as of December 18, 2024.
OpenVINO Model Server performance results are based on release 2024.5, as of November 20, 2024.
The results may not reflect all publicly available updates. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Learn more at intel.com, the OEM, or retailer.
See configuration disclosure for details. No product can be absolutely secure. Performance varies by use, configuration and other factors. Learn more at www.intel.com/PerformanceIndex. Intel optimizations, for Intel compilers or other products, may not optimize to the same degree for non-Intel products.