OpenVINO 2025.2 Available Now!

[Luis_at_Intel]

We are excited to announce the release of OpenVINO™ 2025.2! This update brings expanded model coverage, GPU optimizations, and Gen AI enhancements, designed to maximize the efficiency and performance of your AI deployments, whether at the edge, in the cloud, or locally.

What’s new in this release:

More Gen AI 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™ Ultra 200V Series processors for models with up to 8B parameters is enabled through symmetrical and channel-wise quantization, improving accuracy while maintaining performance efficiency.

Download the 2025.2 Release 
Download Latest Release Now

Get all the details 
See 2025.2 release notes 

NNCF RELEASE

Check out the new NNCF release

Helpful Links

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• Home Page
• Release Notes
• OpenVINO Notebooks
• All Documentation, Guides, and Resources
• System Requirements
• File an Issue on GitHub*