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感谢您注册，了解使用 OpenVINO™ 工具套件进行 AI 推理的最新进展。

OpenVINO™ 工具套件：一款可轻松实现“一次写入，处处部署”的开源 AI 工具套件。

2025.0 版本新增功能特性

OpenVINO 工具套件 2025.0 版通过新功能和性能增强，将您的 AI 部署提升到全新水平。该版本引入了对大语言模型 (LLM) 的持续改进、针对英特尔® 硬件的优化运行时，以及跨边缘、云和本地环境来高效部署 AI 的扩展功能。了解最新资讯，为您的 AI 项目开启全新可能性。

版本说明

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最新特性

Easier Model Access and Conversion

Product	Details
New Model Support	Support for Llama 3.2 (1B and 3B), Gemma 2 (2B and 9B), and YOLO* v11 LLM support on NPUs: Llama 3 8B, Llama 2 7B, Mistral-v0.2-7B, Qwen2-7B-Instruct, and Phi-3 Mini Instruct Preview: Support for Flax, a high-performance Python* neural network library based on JAX*. Its modular design allows for easy customization and accelerated inference on GPUs.

Generative AI and LLM Enhancements

Expanded model support and accelerated inference.

Feature	Details
New Jupyter* Notebooks	Noteworthy notebooks added: Sam2, Llama3.2, Llama3.2 - Vision, Wav2Lip, Whisper, and Llava.
Intel® GPUs GPU Optimizations	Optimizations for built-in GPUs on Intel® Core™ Ultra Processors (Series 1) and Intel® Arc™ Graphics include KV Cache compression for memory reduction along with improved usability and model load time optimizations to improve first token latency for LLMs. Dynamic quantization was enabled to improve first token latency for LLMs on built-in Intel® GPUs without impacting accuracy on Intel Core Ultra processors (Series 1). Second token latency will also improve for large batch inference.
NNCF Updates	The Neural Network Compression Framework (NNCF) implements a new method for generating synthetic text data. This allows LLMs to be compressed more accurately using data-aware methods without datasets.

More Portability and Performance

Develop once, deploy anywhere. OpenVINO toolkit enables developers to run AI at the edge, in the cloud, or locally.

Product	Details
Intel® Hardware Support	Support for Intel® Xeon® 6 processors with P-cores (formerly codenamed Granite Rapids) and Intel Core Ultra 200V processor family (formerly codenamed Arrow Lake-S)
GenAI API Enhancements	Preview: GenAI API enables multimodal AI deployment with support for multimodal pipelines for improved contextual awareness, transcription pipelines for easy audio-to-text conversions, and image generation pipelines for streamlined text-to-visual conversions. A speculative decoding feature was added to the GenAI API for improved performance and efficient text generation using a small draft model that is periodically corrected by the full-size model. Preview: LoRA adapters are now supported in the GenAI API for developers to quickly and efficiently customize image and text generation models for specialized tasks. The GenAI API now also supports LLMs on NPUs, allowing developers to specify NPU as the target device.

Product

Details

Intel® Hardware Support

Support for Intel® Xeon® 6 processors with P-cores (formerly codenamed Granite Rapids) and Intel Core Ultra 200V processor family (formerly codenamed Arrow Lake-S)

GenAI API Enhancements

Preview: GenAI API enables multimodal AI deployment with support for multimodal pipelines for improved contextual awareness, transcription pipelines for easy audio-to-text conversions, and image generation pipelines for streamlined text-to-visual conversions.
A speculative decoding feature was added to the GenAI API for improved performance and efficient text generation using a small draft model that is periodically corrected by the full-size model.
Preview: LoRA adapters are now supported in the GenAI API for developers to quickly and efficiently customize image and text generation models for specialized tasks.
The GenAI API now also supports LLMs on NPUs, allowing developers to specify NPU as the target device.