Open Edge Platform
An Intel-based, partner-enabled, secure, optimized open platform for the edge to accelerate solution development
Modular and Optimized Edge Software Stack
Open Edge Platform 2026.0 is here
Capabilities Highlights
- Composable ingredients for building production-quality multimodal edge AI apps, along with user-friendly workflows for optimizing and deploying models.
- Onboarding, provisioning, and management of edge devices and edge node software, along with orchestration of AI applications and clusters at scale.
- Quickly showcase the performance and features of Intel® hardware for AI at the edge, along with our strategic technology partners.
What's New in 2026.0
AI Suites
- Health and Life Sciences adds a Multimodal Patient Monitoring sample application that analyzes and visualizes real-time sensor fusion signal processing for predictive health monitoring with rPPG, 3 3D Human Pose Estimation, and AI ECG Arrhythmia tracking simulations.
- Education adds Class Summary to Windows systems. Smart Classroom sample application extends Automatic Speech Recognition (ASR Whisper) to diarize teacher-student dialogue with audio / video timestamps for easy navigation.
- Retail enables Order Accuracy sample application for Dine-In restaurants with multi-camera, multi-stream VLM for classifying and detecting wrong orders.
- Metro adds live video summarization based on VLM (Visual Language Models) sending critical event information to alerting systems with Smart NVR sample application on Intel® Core™ Ultra Series 3 Processors.
- Manufacturing adds timestamps for precise queries with the time series microservice and sensor fusion in Weld Defect Detection sample application, along with intelligent batching controls for automating ML pipelines.
- Robotics optimizes integrated AI acceleration and real-time controls on Intel® Core™ Ultra Series 3 processors. Integrates ROS 2 Jazzy Jalisco, D3 and D457 depth cameras for enhanced 3D perception for AMR simulation.
AI Libraries
- OpenVINO™ Gen AI adds VLM pipeline support for Agentic workflows, word-level timestamps for ASR Whisper Pipeline and EAGLE-3 speculative decoding to accelerate LLM inference using small token prediction.
- DL Streamer adds customizable watermark styling for professional presentation, and intelligent batching controls to optimize processing speed and efficiency for AI inference performance. Now seamlessly discovers IP-based camera streams.
- Scenescape aggregates multimodal, time-series sensor fusion data to create dynamic scene graphs with a Scene Controller feature. Tracks up to 1000 objects.
- Geti™ Instant Learn automatically identifies similar objects across datasets for fine-grained detection, classification, and annotation to enable visual question answering of image and text prompt embeddings for instant learning in a low-code GUI.
- Physical AI Studio enables end-to-end VLA workflow for robot calibration, data collection, and policy training (ACT, Pi0, SmolVLA), to support imitation and reinforcement learning, with performance optimization and pipeline deployment on Intel.
- Anomalib Studio s a new low-code framework for visual inspection and anomaly detection using latest Anomalib libraries to accelerate detecting and localizing anomalies within visual data.
Solve the Biggest Challenges at the Edge
Build and Deploy Solutions More Efficiently
Streamline edge solution development and deployment with automation and standard hardware. With the Open Edge Platform, you can achieve working solutions that connect and optimize with minimal effort.
AI Inferencing Optimized for the Edge
Deliver compelling application performance for edge-to-cloud implementations. The Open Edge Platform offers built-in AI runtime optimizations for OpenVINO™ toolkit AI that enable efficient AI inferencing on a wide spectrum of edge hardware.
Infrastructure and Solution Management at Scale
Build infrastructure solutions with Open Edge Platform to securely onboard and manage fleets of edge nodes more efficiently and with lower total cost of ownership. It also allows you to ease operations with dynamic application deployment, near-zero touch provisioning, orchestration, and lifecycle management of edge infrastructure and applications.
Edge AI Suites
Accelerate your solution development and optimization with industry-specific Edge AI Suites.
Retail AI Suite
This suite is an open source application framework designed to accelerate AI workload evaluation and hardware selection for point-of-sale use cases at the edge. This framework helps retailers assess device configurations across Intel product generations to enhance decision-making and reduce the total cost of ownership.
It includes sample applications for use cases such as:
Self Checkout
- Product recognition (detection, classification, and tracking)
- Full pipeline (product, weight, text, and barcode)
- Age verification
Loss Prevention
- Fake scans
- Items in the basket
- Multiproduct identification
- Product switching
- Shopper behavior (hiding items)
- Event video summarization
Order Accuracy
- Object detection using multi-camera computer vision
- Smart video analysis and summarization
- Agentic AI workload
- Advanced LVLM Analysis for Fast Food and Dine-in workflows
Manufacturing AI Suite
This suite is a powerful toolkit for building and scaling AI in industrial environments. The suite simplifies real-time AI development and deployment by using Edge AI technology from Intel.
It supports predictive maintenance, anomaly detection, quality inspection, worker safety, and more. Developers benefit from tools like IoT protocol support, AI analytics libraries, multicamera system software, and closed-loop AI pipelines. Benchmarking resources help evaluate performance across time series, vision, and GenAI use cases.
Sample applications enabled by Manufacturing AI Suite include:
- Predictive maintenance
- Process optimization
- Anomaly detection
- Quality inspection
- Worker safety
- Vision-guided robots
- Operational diagnostics
- Weld defect detection
Metro AI Suite
This suite is an application framework with libraries, tools, and reference implementations, enabling partners to create AI solutions in the video safety and security, transportation, and government edge markets.
Some of the Metro AI Suite components include:
AI Apps for any Intel-Based Edge System
- Advanced video search
- Video summarization
- Natural language search
- Intersection management
- Live video stream analysis
- “Live” avatar interaction
- Smart parking
- Smart tolling
- AI transit route system
- Smart intersection
Benchmarks for Real-World Scenarios
- Benchmarking specifications on Intel® AI Edge Systems
- Platform sizing tool
Tools and Software for AI Acceleration
- AI inference optimization
- Visual and deep learning optimization
- Media processing acceleration
- Edge server video analytics
- Arm* technology to Intel technology conversion
- Sensor fusion-enabled traffic management
Education AI Suite
This preview collection of education-focused AI applications, libraries, and benchmarking tools demonstrate how audio and video recordings from class sessions can be transformed into concise summaries for use in educational settings. Featuring audio-to-speech (ASR) models optimized on OpenVINO™ and streaming media analytics with intelligent pose detection and re-identification (Re-ID) capabilities in DL Streamer, the suite introduces advanced multimodal pipelines to demonstrate high-performance deployment on Intel® CPUs, integrated GPUs, and NPUs.
Audio Intelligence
- Audio transcription with ASR models (Whisper, Paraformer)
- Summarization with LLMs (Qwen, LLaMA)
- Plug-and-play architecture for integrating new ASR and LLM models
- API-first, design-ready, front-end integration
- Extensible roadmap for real-time streaming, diarization, translation, and video analysis
Video Intelligence
- Front Camera Pipeline: Student pose detection: sitting, standing, hand raise, leaning
- Rear Camera Pipeline: Re-Identification (ReID) to track students across camera views
- Board Camera Pipeline: Board content Classifications
Robotics AI Suite
Accelerate development adapting physical AI at the edge and explore new functionality with modular, open-source software components to enable fast development of humanoid, autonomous mobile, and stationary robotics. Sample applications with ROS 2 support, demonstrate vision AI, Gen AI, and media analytics pipelines for enabling robotic manipulation, perception, locomotion, and imitation learning with real-time controls. AI inference engines, and hardware-aware tuning enable fast deployment on Intel silicon.
Collection reference applications, libraries, and pipelines for:
Humanoid
- Imitation Learning – ACT
- LLM Robotics Demo
- ROS 2 and Open Standards
- AI Inference engine
- Hardware-aware tuning
- VSLAM: ORB-SLAM3
Stationary
- Vision & Control
Autonomous Mobile
- ADBScan
- Collaborative SLAM
- Segmentation
- Simulations
- Wandering
- ITFastmapping
- GroundFloor S-Planner
- Multi-camera Demo
- Object Detection
Health and Life Science AI Suite
Right size hardware and prove performance of integrated AI acceleration and real-time performance at the point of care with this collection of clinical AI workloads that combines vitals, signal processing and vision AI pipelines. With four real-world patient monitoring workloads that simulate vitals, track activities, detect arrhythmia in ECG, and monitor blood pressure and heart rate with rPPG, the Patient Monitoring Sample Application shows how concurrent AI workloads perform to benchmark CPU, NPU, and built-in GPU resources predictably at the edge.
Reference Applications
- Multi‑parameter Monitor: Simulate, analyze, and play back metrics and waveforms
- 3D Visual Tracking: Contactless patient presence and activity monitoring
- AI‑ECG Arrhythmia: Signal‑processing with applied AI
- rPPG: Camera‑based heart and respiration monitoring
Models
- Multi-Task Temporal Shift Convolutional Attention Network (MTTS-CAN) model
- Multimodal patient monitoring Data and Control Flows – Metrics Collector Service
Edge AI Libraries
Composable ingredients for building production-quality multimodal edge AI apps, along with user-friendly workflows for optimizing and deploying models.
- Build edge AI applications and use cases that include vision AI, generative AI (GenAI), and time series AI capabilities.
- Get reusable building blocks, with benchmarks, optimized to run performantly on Intel hardware.
- Shorten app development time and simplify model fine-tuning, retraining, and deployment workflows.
Edge Manageability Framework
Onboarding, provisioning, and management of edge devices and edge node software, along with orchestration of AI applications and clusters at scale, using optimized Kubernetes* for the edge.
Zero touch provides increased operational efficiency for enterprises due to reduced cost of truck rolling.
Centralized rapid provisioning and operating system maintenance across distributed devices at scale.
Multitenant centralized management with an encrypt-everywhere security model with single sign-on.
Built-in deep, unique telemetry and observability to drive maintenance planning and automation.
Edge Microvisor Toolkit
Edge Microvisor Toolkit is an open source, edge-optimized, low-footprint, immutable container host that runs modern AI workloads and quickly showcases the performance and features of Intel hardware for AI at the edge, along with our strategic technology partners.
- Immutable Host: A/B updates ensures operational repeatability, reliability, and auditability at scale.
- Partner Ready: Built with all the latest kernel optimizations for easier integration by operating system vendor partners.
- Secure: Trusted compute, single-root I/O virtualization (SR-IOV), secure boot, full disk encryption (FDE), and DM-Verity protect the hypervisor, apps, and data.
Resources
Join the Ecosystem: Develop and Contribute to Open Edge Platform
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