簡介
This package contains the Intel® Distribution of OpenVINO™ Toolkit software version 2026.2 for Linux*, Windows*, and macOS*.
可用的下載項目
- Microsoft Windows*
- 大小:198.3 MB
- SHA256:8BC4394B03CF5AC3D35CE8B1CAF98DE3F2EEC5D186279E1394A18DA18E89A1E5
- macOS*
- 大小:40.4 MB
- SHA256:BBDF887A87642E0176E83733ED2E5B0AECACFDED89612CEFD027FE60CBC30C39
- Microsoft Windows*
- 大小:759.7 MB
- SHA256:DEA432D704EF9653B67527C1F160223F7A7C0F5C4A4351A71259F3E7BB35B708
- Android*
- 大小:81.3 MB
- SHA256:7D3740ECC3C711CC150AB40992C1CF389838A5E0705C0A006377E821E8BE728A
- CentOS Linux 家族*
- 大小:72.4 MB
- SHA256:F4EDDF3CC5158C128124EBDB4351F1C830215C9E7C805BBD13DAAB3EE52D95A9
- Linux*
- 大小:74.6 MB
- SHA256:F56084C029490612A46EB8C3B4DA34F83772CD82E6C6B352CB561B5EDA3B9DF2
- Linux*
- 大小:33 MB
- SHA256:A3DC719697250593A3D21AD663D7D780A22E38FD5E2E033662A5E5672C514DB9
- Ubuntu Family*
- 大小:104.1 MB
- SHA256:86896E9347CD160370D16F80FA2C49C2B7A51EC33B55CEA6493C7DC7C4C61C55
- Ubuntu 24.04 LTS*
- 大小:106.6 MB
- SHA256:7AB44E505154459C374990F73E6732C6937B1CD98E25FEEDC9591AD09459CD87
- Ubuntu 22.04 LTS*
- 大小:39.4 MB
- SHA256:8CE45467967E22FDDB83A6B72A8BD1F9BFA6F43351E1CA2EAF5251064FE17767
詳細說明
What’s New
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More Gen AI coverage and frameworks integrations to minimize code changes
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New models supported: Gemma 4 E2B and Gemma 4 E4B
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Only on CPUs & GPUs: Qwen3-Coder-Next, Qwen3.5, Qwen3.6, Trinity-mini, LFM2-24B-A2B, LFM2-8B-A1B, LFM2.5-350M
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Only on CPUs: YOLO26
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Only on GPUs: Gemma 4 31B and Gemma 4 26B-A4B
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Extended to GPUs: GPT-OSS-120B
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Scaled Dot-Product Attention (SDPA) path support added for LFM2 models
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Support for Hugging Face Transformers v5.0, ensuring compatibility with the latest model architecture for enhanced interoperability.
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Broader LLM model support and more model compression techniques
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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.
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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.
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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.
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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).
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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.
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More portability and performance to run AI at the edge, in the cloud or locally
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OpenVINO™ GenAI extends its JavaScript API to include a Text-to-Speech pipeline and VLM samples for browser and Node.js developers.
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OpenVINO™ Model Server extends tool-calling support to Qwen 3.5 and 3.6 models to enable agentic AI use cases.
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OpenVINO™ Model Server adds streaming transcription support for speech-to-text, reducing latency for real-time voice applications.
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Preview: Introducing OpenVINO Physical AI, a hardware-accelerated, production‑ready inferencing and deployment framework that standardizes how developers connect cameras, robots, models, and safety controls, reducing brittle custom harnesses and making complex systems easier to build, debug, and evolve on Intel platforms.
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Get all the details. See 2026.2 release notes.
Installation instructions
You can choose how to install OpenVINO™ Runtime from Archive* according to your operating system:
- Install OpenVINO Runtime on Linux*
- Install OpenVINO Runtime on Windows*
- Install OpenVINO Runtime on macOS*
What's included in the download package (Archive File)
- Offers both C/C++ and Python APIs
- Additionally includes code samples
Helpful Links
NOTE: Links open in a new window.
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