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[DOCS] benchmark content restructuring-24.4 (#27154)
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111 changes: 41 additions & 70 deletions docs/articles_en/about-openvino/performance-benchmarks.rst
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Expand Up @@ -16,14 +16,12 @@ Performance Benchmarks
Getting Performance Numbers <performance-benchmarks/getting-performance-numbers>


This page presents benchmark results for
This page presents benchmark results for the
`Intel® Distribution of OpenVINO™ toolkit <https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html>`__
and :doc:`OpenVINO Model Server <../openvino-workflow/model-server/ovms_what_is_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.
For a more detailed view of performance numbers for generative AI models, check the
:doc:`Generative AI Benchmark Results <./performance-benchmarks/generative-ai-performance>`

.. grid:: 1 1 2 2
:gutter: 4
Expand All @@ -36,7 +34,7 @@ For a more detailed view of performance numbers for generative AI models, check
:outline:
:expand:

:material-regular:`bar_chart;1.4em` OpenVINO Benchmark Graphs
:material-regular:`bar_chart;1.4em` OpenVINO Benchmark Graphs (general)

.. grid-item::

Expand All @@ -46,10 +44,35 @@ For a more detailed view of performance numbers for generative AI models, check
:outline:
:expand:

:material-regular:`bar_chart;1.4em` OVMS Benchmark Graphs
:material-regular:`bar_chart;1.4em` OVMS Benchmark Graphs (general)

.. grid-item::

.. button-link:: ./performance-benchmarks/generative-ai-performance.html
:class: ov-toolkit-benchmark-genai
:color: primary
:outline:
:expand:

:material-regular:`table_view;1.4em` LLM performance for AI PC

.. grid-item::

.. button-link:: #
:class: ovms-toolkit-benchmark-llm
:color: primary
:outline:
:expand:

:material-regular:`bar_chart;1.4em` OVMS for GenAI (coming soon)






Key performance indicators and workload parameters.

**Key performance indicators and workload parameters**

.. tab-set::

Expand All @@ -65,13 +88,13 @@ Key performance indicators and workload parameters.
.. tab-item:: Latency
:sync: latency

For Vision and NLP models this mhis 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 is started. 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 for
example an industrial robot's response to actions in its environment or obstacle avoidance
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.
Expand All @@ -97,9 +120,10 @@ Key performance indicators and workload parameters.
* input token length: 1024 (the tokens for GenAI models are in English).


.. raw:: html
**Platforms, Configurations, Methodology**

<h2>Platforms, Configurations, Methodology</h2>
To see the methodology used to obtain the numbers and learn how to test performance yourself,
see the guide on :doc:`getting performance numbers <performance-benchmarks/getting-performance-numbers>`.

For a listing of all platforms and configurations used for testing, refer to the following:

Expand Down Expand Up @@ -130,59 +154,10 @@ For a listing of all platforms and configurations used for testing, refer to the
:material-regular:`download;1.5em` Click for Performance Data [XLSX]


The OpenVINO benchmark setup includes a single system with OpenVINO™, as well as the benchmark
application installed. It measures the time spent on actual inference (excluding any pre or post
processing) and then reports on the inferences per second (or Frames Per Second).

OpenVINO™ Model Server (OVMS) employs the Intel® Distribution of OpenVINO™ toolkit runtime
libraries and exposes a set of models via a convenient inference API over gRPC or HTTP/REST.
Its benchmark results are measured with the configuration of multiple-clients-single-server,
using two hardware platforms connected by ethernet. Network bandwidth depends on both platforms
and models used. It is set not to be a bottleneck for workload intensity. The connection is
dedicated only to measuring performance.

.. dropdown:: See more details about OVMS benchmark setup

The benchmark setup for OVMS consists of four main parts:

.. image:: ../assets/images/performance_benchmarks_ovms_02.png
:alt: OVMS Benchmark Setup Diagram

* **OpenVINO™ Model Server** is launched as a docker container on the server platform and it
listens to (and answers) requests from clients. OpenVINO™ Model Server is run on the same
system as the OpenVINO™ toolkit benchmark application in corresponding benchmarking. Models
served by OpenVINO™ Model Server are located in a local file system mounted into the docker
container. The OpenVINO™ Model Server instance communicates with other components via ports
over a dedicated docker network.

* **Clients** are run in separated physical machine referred to as client platform. Clients
are implemented in Python3 programming language based on TensorFlow* API and they work as
parallel processes. Each client waits for a response from OpenVINO™ Model Server before it
will send a new next request. The role played by the clients is also verification of
responses.

* **Load balancer** works on the client platform in a docker container. HAProxy is used for
this purpose. Its main role is counting of requests forwarded from clients to OpenVINO™
Model Server, estimating its latency, and sharing this information by Prometheus service.
The reason of locating the load balancer on the client site is to simulate real life
scenario that includes impact of physical network on reported metrics.

* **Execution Controller** is launched on the client platform. It is responsible for
synchronization of the whole measurement process, downloading metrics from the load
balancer, and presenting the final report of the execution.



.. raw:: html

<h2>Test performance yourself</h2>

You can also test performance for your system yourself, following the guide on
:doc:`getting performance numbers <performance-benchmarks/getting-performance-numbers>`.

.. raw:: html

<h2>Disclaimers</h2>
**Disclaimers**

* Intel® Distribution of OpenVINO™ toolkit performance results are based on release
2024.3, as of July 31, 2024.
Expand All @@ -192,22 +167,18 @@ You can also test performance for your system yourself, following the guide on

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, or from the OEM or retailer.
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 <https://www.intel.com/PerformanceIndex>`__.
Your costs and results may vary.
Intel optimizations, for Intel compilers or other products, may not optimize to the same degree
for non-Intel products.








.. raw:: html

<link rel="stylesheet" type="text/css" href="../_static/css/benchmark-banner.css">
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28 changes: 19 additions & 9 deletions ...articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst
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Expand Up @@ -4,7 +4,7 @@ Most Efficient Large Language Models for AI PC
This page is regularly updated to help you identify the best-performing LLMs on the
Intel® Core™ Ultra processor family and AI PCs.

The tables below list the key performance indicators for a selection of Large Language Models,
The tables below list key performance indicators for a selection of Large Language Models,
running on an Intel® Core™ Ultra 7-165H based system, on built-in GPUs.


Expand All @@ -23,24 +23,34 @@ running on an Intel® Core™ Ultra 7-165H based system, on built-in GPUs.
:class: modeldata stripe
:name: supportedModelsTableOv
:header-rows: 1
:file: ../../_static/download/llm_models.csv
:file: ../../_static/benchmarks_files/llm_models.csv


For complete information on the system config, see:
`Hardware Platforms [PDF] <https://docs.openvino.ai/2024/_static/benchmarks_files/OV-2024.4-platform_list.pdf>`__

To view the data in an editable form, you can download the .csv file here:

.. grid:: 1 1 2 2
:gutter: 4

.. grid-item::

.. button-link:: ../../_static/download/llm_models.csv
All models listed here were tested with the following parameters:

* Framework: PyTorch
* Model precision: INT4
* Beam: 1
* Batch size: 1

.. grid-item::

.. button-link:: https://docs.openvino.ai/2024/_static/benchmarks_files/OV-2024.4-platform_list.pdf
:color: primary
:outline:
:expand:

:material-regular:`download;1.5em` Click for OpenVINO LLM results [CSV]
:material-regular:`download;1.5em` Get full system info [PDF]

.. button-link:: ../../_static/benchmarks_files/llm_models.csv
:color: primary
:outline:
:expand:

:material-regular:`download;1.5em` Get the data in .csv [CSV]

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