openvinotoolkit/openvino 2026.2.0

on GitHub

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 performance to run AI at the edge, in the cloud, or locally.

  • OpenVINO™ GenAI extends its JavaScript API to include a Text-to-Speech pipeline and VLM samples for browser and Node.js developers.
  • Prompt Lookup Decoding extended to vision-language pipelines, delivering significantly faster token generation for multimodal workloads on Intel CPUs and GPUs.
  • OpenVINO™ GenAI now has a smaller runtime footprint after eliminating ICU DLL dependencies from tokenization, leading to reduced memory usage, faster startup, and easier deployment.
  • OpenVINO™ Model Server extends tool-calling support to Qwen 3.5 and 3.6 models to enable agentic AI use cases.
  • OpenVINO™ Model Server adds streaming transcription support for speech-to-text, reducing latency for real-time voice applications.

Support Change and Deprecation Notices

• Discontinued in 2026.0:

  • The deprecated openvino.runtime namespace has been removed. Please use the openvino namespace directly.
  • The deprecated openvino.Type.undefined has been removed. Please use openvino.Type.dynamic instead.
  • The PostponedConstant constructor signature has been updated for improved usability:
    • Old (removed): Callable[[Tensor], None]
    • New: Callable[[], Tensor]
  • The deprecated OpenVINO™ GenAI predefined generation configs were removed.
  • The deprecated OpenVINO GenAI support for whisper stateless decoder model has been removed. Please use a stateful model.
  • The deprecated OpenVINO GenAI StreamerBase put method, bool return type for callbacks, and ChunkStreamer class has been removed.
  • NNCF create_compressed_model() method is now deprecated and removed in 2026. Please use nncf.prune() method for unstructured pruning and nncf.quantize() for INT8 quantization.
  • NNCF optimization methods for TensorFlow models and TensorFlow backend in NNCF are deprecated and removed in 2026. It is recommended to use PyTorch analogous models for training-aware optimization methods and OpenVINO™ IR, PyTorch, and ONNX models for post-training optimization methods from NNCF.
  • The following experimental NNCF methods are deprecated and removed: NAS, Structural Pruning, AutoML, Knowledge Distillation, Mixed-Precision Quantization, Movement Sparsity.
  • CPU plugin now requires support for the AVX2 instruction set as a minimum system requirement. The SSE instruction set will no longer be supported.
  • OpenVINO™ migrated builds based on RHEL 8 to RHEL 9.
  • manylinux2014 upgraded to manylinux_2_28. This aligns with modern toolchain requirements but also means that CentOS 7 will no longer be supported due to glibc incompatibility.

• Deprecated and to be removed in the future:

  • Support for Ubuntu 20.04 has been discontinued due to the end of its standard support.
  • auto shape and auto batch size (reshaping a model in runtime) will be removed in the future. OpenVINO’s dynamic shape models are recommended instead.
  • With the release of Node.js v22, updated Node.js bindings are now available and compatible with the latest LTS version. These bindings do not support CentOS 7, as they rely on newer system libraries unavailable on legacy systems.
  • Starting with 2026.0 release major internal refactoring of the graph iteration mechanism has been implemented for improved performance and maintainability. The legacy path can be enabled by setting the ONNX_ITERATOR=0 environment variable. This legacy path is deprecated and will be removed in future releases.
  • OpenVINO Model Server:
    • The dedicated OpenVINO operator for Kubernetes and OpenShift is now deprecated in favor of the recommended KServe operator. The OpenVINO operator will remain functional in upcoming OpenVINO Model Server releases but will no longer be actively developed. Since KServe provides broader capabilities, no loss of functionality is expected. On the contrary, more functionalities will be accessible and migration between other serving solutions and OpenVINO Model Server will be much easier.
    • TensorFlow Serving (TFS) API support is planned for deprecation. With increasing adoption of the KServe API for classic models and the OpenAI API for generative workloads, usage of the TFS API has significantly declined. Dropping date is to be determined based on the feedback, with a tentative target of mid-2026.
    • Support for Stateful models will be deprecated. These capabilities were originally introduced for Kaldi audio models which is no longer relevant. Current audio models support relies on the OpenAI API, and pipelines implemented via OpenVINO GenAI library.
    • Directed Acyclic Graph Scheduler will be deprecated in favor of pipelines managed by MediaPipe scheduler and will be removed in 2026.3. That approach gives more flexibility, includes wider range of calculators and has support for using processing accelerators.
    • OpenVINO™ GenAI:
      • start_chat() / finish_chat() APIs are deprecated and will be removed in a future major release. Pass a ChatHistory object directly to generate() instead.

You can find OpenVINO™ toolkit 2026.2 release here:

• Download archives* with OpenVINO™
• OpenVINO™ for Python: pip install openvino==2026.2.0

Acknowledgements

Thanks for contributions from the OpenVINO developer community:
@abbyssoul
@aobolensk
@AshutoshSinghIntel
@dbermond
@desertfury
@FarseenSh
@goyaladitya05
@KarSri7694
@Lagmator22
@mostafafaheem
@om4rrr
@pjordanandrsn
@reeseliao
@Sahilbhatane
@ssam18
@sshekhar563
@Vishwa2684
@vmadananth

Release documentation is available here: https://docs.openvino.ai/2026
Release Notes are available here: https://docs.openvino.ai/2026/about-openvino/release-notes-openvino.html