Intel OpenVINO 发布: 2026.4.0

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: Gemma-3n * On CPU, GPU: Kokoro-82M, Qwen3-VL-4B with EAGLE-3, Qwen3-ASR, Muse Glimmer 30B, Qwen3.8 27B, Gemma 4 12B, Hy-MT2-1.8B, DeepSeek OCR-2, and Granite 4.0 H Micro * On NPUs: FLUX.2-Klein 4B and Kokoro-82M * Additional CPU and GPU-enabled models available as early releases: Qwen-Image, Z-Image-Turbo, Granite 4.0 H Tiny, Fun-ASR-Nano, LFM2.5-8B-A1B, MiniCPM5-2B, RF-DETR, BGE Reranker-V2-M3, and BGE M3 * #### Broader LLM model support and more model compression techniques * OpenVINO™ GenAI adds Multi-Token Prediction (MTP) speculative decoding for Gemma 4, Qwen3.5, and Qwen3.6 on CPUs and GPUs, enabling higher throughput and lower latency without sacrificing accuracy. * Preview: OpenVINO™ GenAI introduces DFlash acceleration for Qwen models on GPUs, and visual-token support to reduce latency and speed up GenAI pipelines on Intel® Core™ Ultra Series 3 processors. * With Tree Drafting (Top-K) for EAGLE-3 now supported in OpenVINO™ GenAI, developers can unlock higher throughput on VLM pipelines compared to Chain Drafting (Top-1). * Xe3 integrated graphics optimizations in OpenVINO™ GenAI improve AI inference performance for Gemma 4 models processing long-context inputs on Intel® Core™ Ultra Series 3 processors. * OpenVINO extends Instrumentation and Tracing Technology (ITT) profiling support to the NPU, enabling developers to use Intel® VTune™ Profiler to analyze CPU, GPU, and NPU execution through one consistent toolchain. * #### More portability and performance to run AI at the edge, in the cloud, or locally. * OpenVINO™ GenAI introduces support for ASRPipeline in Node.js, enabling JavaScript developers to run