Openness across software, standards, and silicon is critical for ensuring interoperability, flexibility, and growth.

AI continues to migrate towards the edge and is no longer confined to the data center. Edge AI brings several key advantages, delivering intelligence closer to where data is generated, improving latency for critical functions, ensuring privacy by limiting transmitted data, and reducing energy consumption for AI.

Edge AI encompasses systems performing AI inferencing directly where data is created, including everything from industrial gateways monitoring production lines to smart security cameras in retail stores, connected vehicles on the road, and autonomous robots on warehouse floors. The success of edge AI deployments depends not only on performance or efficiency but also on openness, which enables hardware and software to work seamlessly across vendors and ecosystems.

Why edge AI matters

• **Latency: **Compute speed matters in autonomous driving, robotics, grid control, and many other applications where milliseconds are critical. Local AI inferencing enables real-time responses without relying on network transmission and data center processing time.
• Privacy and security: Local data processing can minimize exposure of sensitive information, helping enterprises meet compliance requirements and maintain data sovereignty.
• Connectivity: Edge environments often face limited or intermittent connectivity. Local intelligence ensures continued operation even when connectivity is interrupted.
• **Energy efficiency: **Edge processing reduces the data sent across networks and lessens reliance on energy-intensive datacenter compute, resulting in overall energy savings. According to the World Economic Forum, processing AI tasks locally at the edge rather than via cloud datacenters can reduce energy consumption by 100x to 1,000x per task.

These advantages are why edge AI is becoming central to industries that demand autonomy, reliability, and real-time intelligence.

Why open source, standards, and interoperability are critical for edge AI

As AI workloads migrate from centralized clouds to distributed edge environments, closed architectures create barriers that limit scalability, interoperability, and innovation. Proprietary software stacks and chip architectures lead to vendor lock-in, reduce flexibility, and slow innovation.

• Fragmentation across vendor ecosystems leads to redundant development efforts, as models must be adjusted for different hardware systems, which lengthens design cycles and increases deployment costs. Open frameworks and standards enable model portability, preventing siloed systems and redundant work.
• Interoperability enables AI workloads to move easily between devices and across vendors. Open interfaces allow companies to mix and match hardware without rewriting code. Scaling across hundreds, thousands, or even millions of devices becomes feasible only when frameworks and standards are open and consistent.

Building an open edge AI deployment from software to silicon

Successful edge AI deployments are built on open-source software, frameworks, and open hardware architectures working together across every layer of the technology stack.

• Open software frameworks like ONNX, TensorFlow Lite, and PyTorch Mobile/Edge enable developers to deploy models across multiple hardware designs. A single model can be optimized once and reused repeatedly, reducing costs and speeding time to deployment.
• Open standards for communication and orchestration such as MQTT, OPC UA, DDS, and Kubernetes-based edge extensions allow systems to share data securely and manage workloads consistently.
• Semiconductor platforms are also embracing open architectures. Architectures like RISC-V allow companies to customize chips, while maintaining compatibility across ecosystems. This flexibility helps avoid dependence on any single instruction set or vendor roadmap.

Open development approaches increase transparency, reduce hidden risks, and ensure that AI systems operate responsibly and predictably.

How open source and standards will enable edge AI growth

The edge AI market is demonstrating steady growth, and its future expansion will depend on adoption of open source frameworks, standards, and interoperable hardware. According to IDC’s Edge AI Processor and Accelerator Forecast, edge processors and accelerators will become a $52 billion market with a five-year CAGR of 16.1% by 2029. Open ecosystems enable faster innovation and easier adaptation to changing hardware and AI models.

Open ecosystems lower risk, extend product life cycles, and improve total cost of ownership by allowing organizations to select the best combination of software and hardware for each use case rather than being confined to a single vendor’s ecosystem.

Common frameworks and standards enable the reuse of proven AI models, shortening development cycles and accelerating edge AI market growth.

With edge AI moving from traditional perception tasks to multimodal, generative, and agentic systems, the complexity of workloads is increasing rapidly. Open interoperable platforms can deliver the scalability and flexibility required to support this evolution.

Organizations evaluating their edge AI strategies should consider prioritizing open-source adoption, standards, and interoperability. Technology providers, from semiconductor vendors to software developers and system integrators, must collaborate to reduce fragmentation, accelerate time-to-market, and reduce total cost of ownership for this shift toward distributed AI.

Nina Turner

(all posts) Nina Turner is a research director on IDC’s Enabling Technologies and Semiconductor team.