Summary of major features and improvements  

• More GenAI coverage and framework integrations to minimize code changes

  • New models supported: Phi-4-mini-reasoning, AFM-4.5B, Gemma-3-1B-it, Gemma-3-4B-it, and Gemma-3-12B.​
  • NPU support added for: Qwen3-1.7B, Qwen3-4B, and Qwen3-8B.
  • LLMs optimized for NPU now available on OpenVINO Hugging Face collection.
  • Preview: Intel® Core™ Ultra Processor and Windows-based AI PCs can now leverage the OpenVINO™ Execution Provider for Windows* ML for high-performance, off-the-shelf starting experience on Windows*.

• Broader LLM model support and more model compression techniques

  • The NPU plug-in adds support for longer contexts of up to 8K tokens, dynamic prompts, and dynamic LoRA for improved LLM performance.
  • The NPU plug-in now supports dynamic batch sizes by reshaping the model to a batch size of 1 and concurrently managing multiple inference requests, enhancing performance and optimizing memory utilization.
  • Accuracy improvements for GenAI models on both built-in and discrete graphics achieved through the implementation of the key cache compression per channel technique, in addition to the existing KV cache per-token compression method.
  • OpenVINO™ GenAI introduces TextRerankPipeline for improved retrieval relevance and RAG pipeline accuracy, plus Structured Output for enhanced response reliability and function calling while ensuring adherence to predefined formats.

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

  • Announcing support for Intel® Arc™ Pro B-Series (B50 and B60).
  • Preview: Hugging Face models that are GGUF-enabled for OpenVINO GenAI are now supported by the OpenVINO™ Model Server for popular LLM model architectures such as DeepSeek Distill, Qwen2, Qwen2.5, and Llama 3. This functionality reduces memory footprint and simplifies integration for GenAI workloads.
  • With improved reliability and tool call accuracy, the OpenVINO™ Model Server boosts support for agentic AI use cases on AI PCs, while enhancing performance on Intel CPUs, built-in GPUs, and NPUs.
  • int4 data-aware weights compression, now supported in the Neural Network Compression Framework (NNCF) for ONNX models, reduces memory footprint while maintaining accuracy and enables efficient deployment in resource-constrained environments.

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.
    • Binary operations Node API has been removed from Python API after previous deprecation.

  • 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:

You can find OpenVINO™ toolkit 2025.3 release here:

• Download archives* with OpenVINO™
• Install it via Conda: conda install -c conda-forge openvino=2025.3.0
• OpenVINO™ for Python: pip install openvino==2025.3.0

Acknowledgements

Thanks for contributions from the OpenVINO developer community: @mahdi-jfri @11happy @arunthakur009 @Vladislav-Denisov @madhurthareja @mohiuddin-khan-shiam @Hmm-1224 @kuanxian1 @johnrhimawan @kinnam888

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