2025.4.0

Summary of major features and improvements  

More GenAI coverage and framework integrations to minimize code changes
- New models supported:
  - On CPUs & GPUs: Qwen3-Embedding-0.6B, Qwen3-Reranker-0.6B, Mistral-Small-24B-Instruct-2501.
  - On NPUs: Gemma-3-4b-it and Qwen2.5-VL-3B-Instruct.
- Preview: Mixture of Experts (MoE) models optimized for CPUs and GPUs, validated for Qwen3-30B-A3B.
- GenAI pipeline integrations: Qwen3-Embedding-0.6B and Qwen3-Reranker-0.6B for enhanced retrieval/ranking, and Qwen2.5VL-7B for video pipeline.
Broader LLM model support and more model compression techniques
- Gold support for Windows ML* enables developers to deploy AI models and applications effortlessly across CPUs, GPUs, and NPUs on Intel® Core™ Ultra processor-powered AI PCs.
- The Neural Network Compression Framework (NNCF) ONNX backend now supports INT8 static post-training quantization (PTQ) and INT8/INT4 weight-only compression to ensure accuracy parity with OpenVINO IR format models. SmoothQuant algorithm support added for INT8 quantization.
- Accelerated multi-token generation for GenAI, leveraging optimized GPU kernels to deliver faster inference, smarter KV-cache reuse, and scalable LLM performance.
- GPU plugin updates include improved performance with prefix caching for chat history scenarios and enhanced LLM accuracy with dynamic quantization support for INT8.
More portability and performance to run AI at the edge, in the cloud, or locally.
- Announcing support for Intel® Core™ Ultra Processor Series 3.
- Encrypted blob format support added for secure model deployment with OpenVINO™ GenAI. Model weights and artifacts are stored and transmitted in an encrypted format, reducing risks of IP theft during deployment. Developers can deploy with minimal code changes using OpenVINO GenAI pipelines.
- OpenVINO™ Model Server and OpenVINO™ GenAI now extend support for Agentic AI scenarios with new features such as output parsing and improved chat templates for reliable multi-turn interactions, and preview functionality for the Qwen3-30B-A3B model. OVMS also introduces a preview for audio endpoints.
- NPU deployment is simplified with batch support, enabling seamless model execution across Intel® Core™ Ultra processors while eliminating driver dependencies. Models are reshaped to batch_size=1 before compilation.
- The improved NVIDIA Triton Server* integration with OpenVINO backend now enables developers to utilize Intel GPUs or NPUs for deployment.

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 runtime namespace for Python API has been marked as deprecated and designated to be removed for 2026.0. The new namespace structure has been delivered, and migration is possible immediately. Details will be communicated through warnings and via documentation.
  - Binary operations Node API has been removed from Python API after previous deprecation.
  - PostponedConstant Python API Update: The PostponedConstant constructor signature is changing for better usability. Update maker from Callable[[Tensor], None] to Callable[[], Tensor]. The old signature will be removed in version 2026.0.
- 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.
- Deprecated OpenVINO Model Server (OVMS) benchmark client in C++ using TensorFlow Serving API.
- NPU Device Plugin:
  - Removed logic to detect and handle Intel® Core™ Ultra Processors (Series 1) drivers older than v1688. Since v1688 is the earliest officially supported driver, older versions (e.g., v1477) are no longer recommended or supported.

You can find OpenVINO™ toolkit 2025.4 release here:

Download archives* with OpenVINO™
Install it via Conda: conda install -c conda-forge openvino=2025.4.0
OpenVINO™ for Python: pip install openvino==2025.4.0

Acknowledgements

Thanks for contributions from the OpenVINO developer community: @arunthakur009 @mahdi-jfri @nashez @RudraCodesForU @Sujanian1304 @Vladislav-Denisov

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: Qwen3-Embedding-0.6B, Qwen3-Reranker-0.6B, Mistral-Small-24B-Instruct-2501.
  - On NPUs: Gemma-3-4b-it and Qwen2.5-VL-3B-Instruct.
- Preview: Mixture of Experts (MoE) models optimized for CPUs and GPUs, validated for Qwen3-30B-A3B.
- GenAI pipeline integrations: Qwen3-Embedding-0.6B and Qwen3-Reranker-0.6B for enhanced retrieval/ranking, and Qwen2.5VL-7B for video pipeline.
Broader LLM model support and more model compression techniques
- Gold support for Windows ML* enables developers to deploy AI models and applications effortlessly across CPUs, GPUs, and NPUs on Intel® Core™ Ultra processor-powered AI PCs.
- The Neural Network Compression Framework (NNCF) ONNX backend now supports INT8 static post-training quantization (PTQ) and INT8/INT4 weight-only compression to ensure accuracy parity with OpenVINO IR format models. SmoothQuant algorithm support added for INT8 quantization.
- Accelerated multi-token generation for GenAI, leveraging optimized GPU kernels to deliver faster inference, smarter KV-cache reuse, and scalable LLM performance.
- GPU plugin updates include improved performance with prefix caching for chat history scenarios and enhanced LLM accuracy with dynamic quantization support for INT8.
More portability and performance to run AI at the edge, in the cloud, or locally.
- Announcing support for Intel® Core™ Ultra Processor Series 3.
- Encrypted blob format support added for secure model deployment with OpenVINO™ GenAI. Model weights and artifacts are stored and transmitted in an encrypted format, reducing risks of IP theft during deployment. Developers can deploy with minimal code changes using OpenVINO GenAI pipelines.
- OpenVINO™ Model Server and OpenVINO™ GenAI now extend support for Agentic AI scenarios with new features such as output parsing and improved chat templates for reliable multi-turn interactions, and preview functionality for the Qwen3-30B-A3B model. OVMS also introduces a preview for audio endpoints.
- NPU deployment is simplified with batch support, enabling seamless model execution across Intel® Core™ Ultra processors while eliminating driver dependencies. Models are reshaped to batch_size=1 before compilation.
- The improved NVIDIA Triton Server* integration with OpenVINO backend now enables developers to utilize Intel GPUs or NPUs for deployment.

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 runtime namespace for Python API has been marked as deprecated and designated to be removed for 2026.0. The new namespace structure has been delivered, and migration is possible immediately. Details will be communicated through warnings and via documentation.
  - Binary operations Node API has been removed from Python API after previous deprecation.
  - PostponedConstant Python API Update: The PostponedConstant constructor signature is changing for better usability. Update maker from Callable[[Tensor], None] to Callable[[], Tensor]. The old signature will be removed in version 2026.0.
- 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.
- Deprecated OpenVINO Model Server (OVMS) benchmark client in C++ using TensorFlow Serving API.
- NPU Device Plugin:
  - Removed logic to detect and handle Intel® Core™ Ultra Processors (Series 1) drivers older than v1688. Since v1688 is the earliest officially supported driver, older versions (e.g., v1477) are no longer recommended or supported.

You can find OpenVINO™ toolkit 2025.4 release here:

Download archives* with OpenVINO™
Install it via Conda: conda install -c conda-forge openvino=2025.4.0
OpenVINO™ for Python: pip install openvino==2025.4.0

Acknowledgements

Thanks for contributions from the OpenVINO developer community: @arunthakur009 @mahdi-jfri @nashez @RudraCodesForU @Sujanian1304 @Vladislav-Denisov

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:

NPU Device Plugin:

More C++ Projects

tensorflow

llama.cpp

electron

2025.4.0

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:

NPU Device Plugin:

More C++ Projects

tensorflow

llama.cpp

electron

godot