Intel OpenVINO 发布: 2026.2.0

来源摘要

### Summary of major features and improvements * #### More GenAI coverage and framework integrations to minimize code changes * New models supported: Gemma 4 E2B and Gemma 4 E4B * Only on CPUs & GPUs: Qwen3-Coder-Next, Qwen3.5, Qwen3.6, Trinity-mini, LFM2-24B-A2B, LFM2-8B-A1B, LFM2.5-350M​ * Only on CPUs: YOLO26 * Only on GPUs: Gemma 4 31B and Gemma 4 26B-A4B * Extended to GPUs: GPT-OSS-120B * Scaled Dot-Product Attention (SDPA) path support added for LFM2 models * Support for Hugging Face Transformers v5.0, ensuring compatibility with the latest model architecture for enhanced interoperability. * #### Broader LLM model support and more model compression techniques * OpenVINO™ GenAI introduces extension support for loading custom extension libraries and registering unsupported operations via the extensions property. This gives developers the flexibility to run models with custom ops that OpenVINO doesn't support out of the box. * INT4 KV-cache compression is enabled for GPUs, with substantial memory reduction when KV cache size is significant, such as with large input prompts exceeding 32K tokens. * OpenVINO GenAI significantly reduces model loading times on GPU when using cache blobs — preventing bottlenecks for multi-stage AI pipelines, including agentic use cases that rely on multiple models. * Optimized IR read mode with independently managed constant buffers to reduce peak memory usage by avoiding unnecessary duplication of weight data unless required for correctness (Linux support added in this release). * Preview: Enhanced XAttention accuracy on CPUs and GPUs through by-channel INT8 KV-cache quantization (compared to by-token INT8 KV-cache), matching the default by-channel INT8 KV cache quantization when XAttention is not enabled. * #### More portability and perf