Intel® Distribution of OpenVINO™ Toolkit

What's New

• More Gen AI coverage and frameworks 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 are now available on the 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 optimization 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 were 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.

Get all the details. See 2025.3 release notes.

Installation instructions

You can choose how to install OpenVINO™ Runtime from Archive* according to your operating system:

• Install OpenVINO Runtime on Linux*
• Install OpenVINO Runtime on Windows*
• Install OpenVINO Runtime on macOS*

What's included in the download package (Archive File)

• Offers both C/C++ and Python APIs
• Additionally includes code samples

Helpful Links

NOTE: Links open in a new window.

• Home Page
• Download
• All Documentation, Guides, and Resources
• OpenVINO Notebooks
• Release Notes
• Community Forum
• File an Issue on GitHub*