2025.2.0

Summary of major features and improvements  

More GenAI coverage and framework integrations to minimize code changes
- New models supported on CPUs & GPUs: Phi-4, Mistral-7B-Instruct-v0.3, SD-XL Inpainting 0.1, Stable Diffusion 3.5 Large Turbo, Phi-4-reasoning, Qwen3, and Qwen2.5-VL-3B-Instruct. Mistral 7B Instruct v0.3 is also supported on NPUs.
- Preview: OpenVINO ™ GenAI introduces a text-to-speech pipeline for the SpeechT5 TTS model, while the new RAG backend offers developers a simplified API that delivers reduced memory usage and improved performance.
- Preview: OpenVINO™ GenAI offers a GGUF Reader for seamless integration of llama.cpp based LLMs, with Python and C++ pipelines that load GGUF models, build OpenVINO graphs, and run GPU inference on-the-fly. Validated for popular models: DeepSeek-R1-Distill-Qwen (1.5B, 7B), Qwen2.5 Instruct (1.5B, 3B, 7B) & llama-3.2 Instruct (1B, 3B, 8B).
Broader LLM model support and more model compression techniques
- Further optimization of LoRA adapters in OpenVINO GenAI for improved LLM, VLM, and text-to-image model performance on built-in GPUs. Developers can use LoRA adapters to quickly customize models for specialized tasks.
- KV cache compression for CPUs is enabled by default for INT8, providing a reduced memory footprint while maintaining accuracy compared to FP16. Additionally, it delivers substantial memory savings for LLMs with INT4 support compared to INT8.
- Optimizations for Intel® Core™ Ultra Processor Series 2 built-in GPUs and Intel® Arc™ B Series Graphics with the Intel® XMX systolic platform to enhance the performance of VLM models and hybrid quantized image generation models, as well as improve first-token latency for LLMs through dynamic quantization.
More portability and performance to run AI at the edge, in the cloud, or locally.
- Enhanced Linux* support with the latest GPU driver for built-in GPUs on Intel® Core™ Ultra Processor Series 2 (formerly codenamed Arrow Lake H).
- OpenVINO™ Model Server now offers a streamlined C++ version for Windows and enables improved performance for long-context models through prefix caching, and a smaller Windows package that eliminates the Python dependency. Support for Hugging Face models is now included.
- Support for INT4 data-free weights compression for ONNX models implemented in the Neural Network Compression Framework (NNCF).
- NPU support for FP16-NF4 precision on Intel® Core™ 200V Series processors for models with up to 8B parameters is enabled through symmetrical and channel-wise quantization, improving accuracy while maintaining performance efficiency.

Support Change and Deprecation Notices

Discontinued in 2025:
- Runtime components:
  - The OpenVINO property of Affinity API is no longer available. It has been replaced with CPU binding configurations (ov::hint::enable_cpu_pinning).
  - The openvino-nightly PyPI module has been discontinued. End-users should proceed with the Simple PyPI nightly repo instead. More information in Release Policy.The openvino-nightly PyPI module has been discontinued. End-users should proceed with the Simple PyPI nightly repo instead. More information in Release Policy.
- Tools:
  - The OpenVINO™ Development Tools package (pip install openvino-dev) is no longer available for OpenVINO releases in 2025.
  - Model Optimizer is no longer available. Consider using the new conversion methods instead. For more details, see the model conversion transition guide.
  - Intel® Streaming SIMD Extensions (Intel® SSE) are currently not enabled in the binary package by default. They are still supported in the source code form.
  - Legacy prefixes: l_, w_, and m_ have been removed from OpenVINO archive names.
- OpenVINO GenAI:
  - StreamerBase::put(int64_t token)
  - The Bool value for Callback streamer is no longer accepted. It must now return one of three values of StreamingStatus enum.
  - ChunkStreamerBase is deprecated. Use StreamerBase instead.
- NNCF create_compressed_model() method is now deprecated. nncf.quantize() method is recommended for Quantization-Aware Training of PyTorch and TensorFlow models.
- OpenVINO Model Server (OVMS) benchmark client in C++ using TensorFlow Serving API.
Deprecated and to be removed in the future:
- Python 3.9 is now deprecated and will be unavailable after OpenVINO version 2025.4.
- openvino.Type.undefined is now deprecated and will be removed with version 2026.0. openvino.Type.dynamic should be used instead.
- APT & YUM Repositories Restructure: Starting with release 2025.1, users can switch to the new repository structure for APT and YUM, which no longer uses year-based subdirectories (like “2025”). The old (legacy) structure will still be available until 2026, when the change will be finalized. Detailed instructions are available on the relevant documentation pages:
  - Installation guide - yum
  - Installation guide - apt
- OpenCV binaries will be removed from Docker images in 2026.
- Ubuntu 20.04 support will be deprecated in future OpenVINO releases due to the end of standard support.
- “auto shape” and “auto batch size” (reshaping a model in runtime) will be removed in the future. OpenVINO’s dynamic shape models are recommended instead.
- MacOS x86 is no longer recommended for use due to the discontinuation of validation. Full support will be removed later in 2025.
- The openvino namespace of the OpenVINO Python API has been redesigned, removing the nested openvino.runtime module. The old namespace is now considered deprecated and will be discontinued in 2026.0.

You can find OpenVINO™ toolkit 2025.2 release here:

Download archives* with OpenVINO™
Install it via Conda: conda install -c conda-forge openvino=2025.2.0
OpenVINO™ for Python: pip install openvino==2025.2.0

Acknowledgements

Thanks for contributions from the OpenVINO developer community: @11happy @rahulchaphalkar @sanleo-wq @ashwins990 @NingLi670 @mohame54 @chiruu12 @SuperChamp234 @ChrisAB @kimgeonsu @code-dev05 @Mohamed-Ashraf273 @arunthakur009 @Captain-MUDIT @Simonwzm @Hmm-1224 @srinjoydutta03 @hridaya14 @victorgearhead @Huanli-Gong @Imokutmfon

Release documentation is available here: https://docs.openvino.ai/2025 Release Notes are available here: https://docs.openvino.ai/2025/about-openvino/release-notes-openvino.html

Summary of major features and improvements  

More GenAI coverage and framework integrations to minimize code changes
- New models supported on CPUs & GPUs: Phi-4, Mistral-7B-Instruct-v0.3, SD-XL Inpainting 0.1, Stable Diffusion 3.5 Large Turbo, Phi-4-reasoning, Qwen3, and Qwen2.5-VL-3B-Instruct. Mistral 7B Instruct v0.3 is also supported on NPUs.
- Preview: OpenVINO ™ GenAI introduces a text-to-speech pipeline for the SpeechT5 TTS model, while the new RAG backend offers developers a simplified API that delivers reduced memory usage and improved performance.
- Preview: OpenVINO™ GenAI offers a GGUF Reader for seamless integration of llama.cpp based LLMs, with Python and C++ pipelines that load GGUF models, build OpenVINO graphs, and run GPU inference on-the-fly. Validated for popular models: DeepSeek-R1-Distill-Qwen (1.5B, 7B), Qwen2.5 Instruct (1.5B, 3B, 7B) & llama-3.2 Instruct (1B, 3B, 8B).
Broader LLM model support and more model compression techniques
- Further optimization of LoRA adapters in OpenVINO GenAI for improved LLM, VLM, and text-to-image model performance on built-in GPUs. Developers can use LoRA adapters to quickly customize models for specialized tasks.
- KV cache compression for CPUs is enabled by default for INT8, providing a reduced memory footprint while maintaining accuracy compared to FP16. Additionally, it delivers substantial memory savings for LLMs with INT4 support compared to INT8.
- Optimizations for Intel® Core™ Ultra Processor Series 2 built-in GPUs and Intel® Arc™ B Series Graphics with the Intel® XMX systolic platform to enhance the performance of VLM models and hybrid quantized image generation models, as well as improve first-token latency for LLMs through dynamic quantization.
More portability and performance to run AI at the edge, in the cloud, or locally.
- Enhanced Linux* support with the latest GPU driver for built-in GPUs on Intel® Core™ Ultra Processor Series 2 (formerly codenamed Arrow Lake H).
- OpenVINO™ Model Server now offers a streamlined C++ version for Windows and enables improved performance for long-context models through prefix caching, and a smaller Windows package that eliminates the Python dependency. Support for Hugging Face models is now included.
- Support for INT4 data-free weights compression for ONNX models implemented in the Neural Network Compression Framework (NNCF).
- NPU support for FP16-NF4 precision on Intel® Core™ 200V Series processors for models with up to 8B parameters is enabled through symmetrical and channel-wise quantization, improving accuracy while maintaining performance efficiency.

Support Change and Deprecation Notices

Discontinued in 2025:
- Runtime components:
  - The OpenVINO property of Affinity API is no longer available. It has been replaced with CPU binding configurations (ov::hint::enable_cpu_pinning).
  - The openvino-nightly PyPI module has been discontinued. End-users should proceed with the Simple PyPI nightly repo instead. More information in Release Policy.The openvino-nightly PyPI module has been discontinued. End-users should proceed with the Simple PyPI nightly repo instead. More information in Release Policy.
- Tools:
  - The OpenVINO™ Development Tools package (pip install openvino-dev) is no longer available for OpenVINO releases in 2025.
  - Model Optimizer is no longer available. Consider using the new conversion methods instead. For more details, see the model conversion transition guide.
  - Intel® Streaming SIMD Extensions (Intel® SSE) are currently not enabled in the binary package by default. They are still supported in the source code form.
  - Legacy prefixes: l_, w_, and m_ have been removed from OpenVINO archive names.
- OpenVINO GenAI:
  - StreamerBase::put(int64_t token)
  - The Bool value for Callback streamer is no longer accepted. It must now return one of three values of StreamingStatus enum.
  - ChunkStreamerBase is deprecated. Use StreamerBase instead.
- NNCF create_compressed_model() method is now deprecated. nncf.quantize() method is recommended for Quantization-Aware Training of PyTorch and TensorFlow models.
- OpenVINO Model Server (OVMS) benchmark client in C++ using TensorFlow Serving API.
Deprecated and to be removed in the future:
- Python 3.9 is now deprecated and will be unavailable after OpenVINO version 2025.4.
- openvino.Type.undefined is now deprecated and will be removed with version 2026.0. openvino.Type.dynamic should be used instead.
- APT & YUM Repositories Restructure: Starting with release 2025.1, users can switch to the new repository structure for APT and YUM, which no longer uses year-based subdirectories (like “2025”). The old (legacy) structure will still be available until 2026, when the change will be finalized. Detailed instructions are available on the relevant documentation pages:
  - Installation guide - yum
  - Installation guide - apt
- OpenCV binaries will be removed from Docker images in 2026.
- Ubuntu 20.04 support will be deprecated in future OpenVINO releases due to the end of standard support.
- “auto shape” and “auto batch size” (reshaping a model in runtime) will be removed in the future. OpenVINO’s dynamic shape models are recommended instead.
- MacOS x86 is no longer recommended for use due to the discontinuation of validation. Full support will be removed later in 2025.
- The openvino namespace of the OpenVINO Python API has been redesigned, removing the nested openvino.runtime module. The old namespace is now considered deprecated and will be discontinued in 2026.0.

You can find OpenVINO™ toolkit 2025.2 release here:

Download archives* with OpenVINO™
Install it via Conda: conda install -c conda-forge openvino=2025.2.0
OpenVINO™ for Python: pip install openvino==2025.2.0

Acknowledgements

Release documentation is available here: https://docs.openvino.ai/2025 Release Notes are available here: https://docs.openvino.ai/2025/about-openvino/release-notes-openvino.html

openvino

Summary of major features and improvements

More GenAI coverage and framework integrations to minimize code changes

Broader LLM model support and more model compression techniques

More portability and performance to run AI at the edge, in the cloud, or locally.

Support Change and Deprecation Notices

Runtime components:

Tools:

OpenVINO GenAI:

More C++ Projects

tensorflow

electron

llama.cpp

godot

2025.2.0

Summary of major features and improvements

More GenAI coverage and framework integrations to minimize code changes

Broader LLM model support and more model compression techniques

More portability and performance to run AI at the edge, in the cloud, or locally.

Support Change and Deprecation Notices

Runtime components:

Tools:

OpenVINO GenAI:

More C++ Projects

tensorflow

electron

llama.cpp

godot