github openvinotoolkit/openvino 2026.0.0
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4 hours ago

Summary of major features and improvements  

  • More GenAI coverage and framework integrations to minimize code changes

    • New models supported on CPUs & GPUs: GPT-OSS-20B, MiniCPM-V-4_5-8B, and MiniCPM-o-2.6​
    • New models supported on NPUs: MiniCPM-o-2.6. In addition, NPU support is now available on Qwen2.5-1B-Instruct, Qwen3-Embedding-0.6B, Qwen-2.5-coder-0.5B.​
    • OpenVINO™ GenAI now adds word-level timestamp functionality to the Whisper Pipeline on CPUs, GPUs, and NPUs, enabling more accurate transcriptions and subtitling in line with OpenAI and FasterWhisper implementations.​
    • Phi-3-mini FastDraft model is now available on Hugging Face to accelerate LLM inference on NPUs. FastDraft optimizes speculative decoding for LLMs.

  • Broader LLM model support and more model compression techniques

    • With the new int4 data-aware weight compression for 3D MatMuls, the Neural Network Compression Framework enables MoE LLMs to run with reduced memory, bandwidth, and improved accuracy compared to data-free schemes-delivering faster, more efficient deployment on resource-constrained devices.​
    • The Neural Network Compression Framework now supports per-layer and per-group Look-Up Tables (LUT) for FP8-4BLUT quantization. This enables fine-grained, codebook-based compression that reduces model size and bandwidth while improving inference speed and accuracy for LLMs and transformer workloads.

  • More portability and performance to run AI at the edge, in the cloud, or locally.

    • OpenVINO™ GenAI adds VLM pipeline support to enhance Agentic AI framework integration.​
    • OpenVINO GenAI now supports speculative decoding for NPUs, delivering improved performance and efficient text generation through a small draft model that is periodically validated by the full-size model.​
    • Preview: NPU compiler integration with the NPU plugin enables ahead-of-time and on-device compilation without relying on OEM driver updates. Developers can enable this feature for a single, ready-to-ship package that reduces integration friction and accelerates time-to-value.​
    • OpenVINO™ Model Server adds enhanced support for audio endpoint plus agentic continuous batching and concurrent runs for improved LLM performance in agentic workflows on Intel CPUs and GPUs.

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.
    • MacOS x86 is no longer supported.
    • APT & YUM Repositories Restructure: Starting with release 2025.1, users can switch to the new repository structure for APT and YUM, which no longer uses year-based subdirectories (like “2025”). The old (legacy) structure is unavailable starting 2026.0. Detailed instructions are available on the relevant documentation pages:
    • OpenCV binaries removed from Docker images.

  • 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.

You can find OpenVINO™ toolkit 2026.0 release here:

Acknowledgements

Thanks for contributions from the OpenVINO developer community:
@Sujanian1304
@Nishant-ZFYII
@srinjoydutta03
@samthakur587
@IamRam3

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

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