- Update to 2025.0.0
- More GenAI coverage and framework integrations to minimize code
changes
* New models supported: Qwen 2.5, Deepseek-R1-Distill-Llama-8B,
DeepSeek-R1-Distill-Qwen-7B, and DeepSeek-R1-Distill-Qwen-1.5B,
FLUX.1 Schnell and FLUX.1 Dev
* Whisper Model: Improved performance on CPUs, built-in GPUs,
and discrete GPUs with GenAI API.
* Preview: Introducing NPU support for torch.compile, giving
developers the ability to use the OpenVINO backend to run the
PyTorch API on NPUs. 300+ deep learning models enabled from
the TorchVision, Timm, and TorchBench repositories..
- Broader Large Language Model (LLM) support and more model
compression techniques.
* Preview: Addition of Prompt Lookup to GenAI API improves 2nd
token latency for LLMs by effectively utilizing predefined
prompts that match the intended use case.
* Preview: The GenAI API now offers image-to-image inpainting
functionality. This feature enables models to generate
realistic content by inpainting specified modifications and
seamlessly integrating them with the original image.
* Asymmetric KV Cache compression is now enabled for INT8 on
CPUs, resulting in lower memory consumption and improved 2nd
token latency, especially when dealing with long prompts that
require significant memory. The option should be explicitly
specified by the user.
- More portability and performance to run AI at the edge, in the
cloud, or locally.
* Support for the latest Intel® Core™ Ultra 200H series
processors (formerly codenamed Arrow Lake-H)
* Integration of the OpenVINO ™ backend with the Triton
Inference Server allows developers to utilize the Triton
server for enhanced model serving performance when deploying
on Intel CPUs.
* Preview: A new OpenVINO ™ backend integration allows
developers to leverage OpenVINO performance optimizations
directly within Keras 3 workflows for faster AI inference on
CPUs, built-in GPUs, discrete GPUs, and NPUs. This feature is
available with the latest Keras 3.8 release.
* The OpenVINO Model Server now supports native Windows Server
deployments, allowing developers to leverage better
performance by eliminating container overhead and simplifying
GPU deployment.
- Support Change and Deprecation Notices
* Now deprecated:
+ Legacy prefixes l_, w_, and m_ have been removed from
OpenVINO archive names.
+ The runtime namespace for Python API has been marked as
deprecated and designated to be removed for 2026.0. The
new namespace structure has been delivered, and migration
is possible immediately. Details will be communicated
through warnings andvia documentation.
+ NNCF create_compressed_model() method is deprecated.
nncf.quantize() method is now recommended for
Quantization-Aware Training of PyTorch and
TensorFlow models.
OBS-URL: https://build.opensuse.org/request/show/1244529
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=25
