OpenVINO 2026.0.0 release!

https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html?PACKAGE=OPENVINO_BASE&VERSION=v_2026_0_0&OP_SYSTEM=LINUX&DISTRIBUTION=PIP

This release includes features that improve GenAI model performance and accuracy while reducing friction to accelerate time-to-value.

Key Highlights:
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More Gen AI coverage and framework integrations to minimize code changes:
- New models supported on CPUs & GPUs: GPT-OSS-20B, Qwen3-30B-A3B, 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.
- Preview: OpenVINO™ GenAI adds support for video generation pipeline based on LTX-Video model on CPU and GPUs.
- 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.

Broader LLM model support and more model compression techniques:
- OpenVINO™ GenAI and OpenVINO™ Model Server introduces EAGLE-3 speculative decoding to accelerate LLM inference using smarter token prediction on Intel CPUs and GPUs. Validated on Qwen3-8B model.
- 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.
- Preview: 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.

#intel #openvino

Fazit nach paar Stunden #frigate mit #openvino

Hunde und Katzen zu unterscheiden fällt der LLM ganz schön schwer. Besonders wenn Kater #cooper durchs Bild marschiert. Der ist halt echt schwarz und das kann openvino schwer kompensieren. Man sieht, dass es da auch unsicherheiten gibt. Manchmal nur knapp über 50%. Personen hingegen werden zu knapp 100% erkannt.

UP Xtreme ARL AI Dev Kit review – Benchmarks and AI workloads on an Intel Core Ultra 5 225H Arrow Lake SBC

https://web.brid.gy/r/https://www.cnx-software.com/2025/12/14/up-xtreme-arl-ai-dev-kit-review-benchmarks-and-ai-workloads-on-an-intel-core-ultra-5-225h-arrow-lake-sbc/

#Ollama #OpenVINO #IntelNPU Người dùng tìm cách kết nối Ollama với OpenVINO (AI inference) trên thiết bị hỗ trợ Intel NPU. Tính năng còn sơ khai, thiếu thông tin cụ thể. Cần chia sẻ kinh nghiệm cài đặt/Docker? #CôngNghệAI #Intel #OllamaVi

https://www.reddit.com/r/ollama/comments/1pho3g7/ollama_openvino/

Fanless AIM101 Edge System Integrates Intel Processor N150 and PCIe AI Accelerator Support

https://fed.brid.gy/r/https://linuxgizmos.com/fanless-aim101-edge-system-integrates-intel-processor-n150-and-pcie-ai-accelerator-support/

Личное облако на Proxmox, нейронки, LLM и embedding

В прошлой статье я описывал как построить сетевую часть самодержавного сервера, пора принести в него что-то отдаленно разумное. Это руководство описывает весь процесс: от подготовки хоста Proxmox и настройки LXC-контейнера до поиска, конвертации и запуска embedding-моделей (на примере BAAI/bge-large-en-v1.5 ) с использованием Intel GPU для работы модели. Но будет легко запустить не одну модель или полноценного чатбота на этой основе. Главное, что будет ясно как использовать даже простое имеющееся железо домашнего сервера для этого.

https://habr.com/ru/articles/967418/

#proxmox #lxc #openvino #ml #gpu_passthrough #embeddings

https://github.com/openvinotoolkit/openvino

#openvino #toolkit #AI