Intel OpenVINO 发布: 2026.3.0

来源摘要

### Summary of major features and improvements * #### More GenAI coverage and framework integrations to minimize code changes * New models supported: * On CPU, GPU, and NPU: SmolLM3-3B, LFM2-1.2B, LFM2.5-1.2B * On CPU and GPU: Harrier OSS-v1-0.6B, Qwen3-8B with EAGLE-3, MiniCPM5-1B, FLUX.2-klein-4B * Extended to GPU and NPU: YOLO26 * Additional models available as early releases on CPU and GPU: Qwen3-ASR, Qwen3-Omni, Gemma-3n, Qwen3-VL-Embedding-8B, Kokoro-82M * Support for Hugging Face Transformers v5.5, ensuring compatibility with the latest model architectures on Hugging Face * #### Broader LLM model support and more model compression techniques * OpenVINO™ GenAI extends the EAGLE-3 speculative decoding pipeline to LLMs and VLMs, enhancing existing continuous batching and adding Top-K sampling to deliver additional token-generation speedups on CPUs, GPUs, and NPUs. * Lazy weight loading is enabled for IR and ONNX models to automatically select an optimal loading and compilation path, minimizing peak memory usage during model initialization. * Neural Network Compression Framework (NNCF) now supports FP8 quantization for ONNX models, helping developers realize FP8 performance, accuracy, and memory gains while expanding low-precision inference options for production deployments. * OpenVINO™ GenAI now supports three additional pipelines: Omni for multimodal workloads, ASR for speech recognition, and Embedding for multimodal embedding generation. * #### More portability and performance to run AI at the edge, in the cloud, or locally. * Introducing support for Intel® Xeon® 6+ processors (formerly codenamed Clearwater Forest) * MoE offloading to disk enabled, allowing 30B MoE models like Qwen3-30B-A3B to run even on devices with 16 GB of memory while maintaining acceptable