Alessandro de Oliveira Faria
63929e8cbb
- More GenAI coverage and framework integrations to minimize code
changes
* New models supported: Phi-4-mini-reasoning, AFM-4.5B,
Gemma-3-1B-it, Gemma-3-4B-it, and Gemma-3-12B,
* NPU support added for: Qwen3-1.7B, Qwen3-4B, and Qwen3-8B.
* LLMs optimized for NPU now available on OpenVINO Hugging
Face collection.
- Broader LLM model support and more model compression techniques
* The NPU plug-in adds support for longer contexts of up to
8K tokens, dynamic prompts, and dynamic LoRA for improved
LLM performance.
* The NPU plug-in now supports dynamic batch sizes by reshaping
the model to a batch size of 1 and concurrently managing
multiple inference requests, enhancing performance and
optimizing memory utilization.
* Accuracy improvements for GenAI models on both built-in
and discrete graphics achieved through the implementation
of the key cache compression per channel technique, in
addition to the existing KV cache per-token compression
method.
* OpenVINO™ GenAI introduces TextRerankPipeline for improved
retrieval relevance and RAG pipeline accuracy, plus
Structured Output for enhanced response reliability and
function calling while ensuring adherence to predefined
formats.
- More portability and performance to run AI at the edge,
in the cloud, or locally.
* Announcing support for Intel® Arc™ Pro B-Series
(B50 and B60).
* Preview: Hugging Face models that are GGUF-enabled for
OpenVINO GenAI are now supported by the OpenVINO™ Model
Server for popular LLM model architectures such as
DeepSeek Distill, Qwen2, Qwen2.5, and Llama 3.
This functionality reduces memory footprint and
simplifies integration for GenAI workloads.
* With improved reliability and tool call accuracy,
the OpenVINO™ Model Server boosts support for
agentic AI use cases on AI PCs, while enhancing
performance on Intel CPUs, built-in GPUs, and NPUs.
* int4 data-aware weights compression, now supported in the
Neural Network Compression Framework (NNCF) for ONNX
models, reduces memory footprint while maintaining
accuracy and enables efficient deployment in
resource-constrained environments.
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=42
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5 lines
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name: openvino
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version: 2025.3.0
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mtime: 1756212984
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commit: 44526285f241251e9543276572676365fbe542a4
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