Edge AI

Edge AI combines AI with edge computing, allowing AI models to run directly on devices such as smartphones, IoT devices, or embedded systems. This means data can be analyzed and decisions can be made locally, even offline, without sending information back to a central data center.

This approach not only reduces latency and bandwidth usage but also enables devices to make intelligent decisions on-site, responding to their environment in real time. However, edge environments are often limited by hardware and power constraints, making it a key challenge to run AI efficiently under these conditions.

Fortunately, advances in hardware performance, along with AI models becoming more lightweight through techniques like distillation and pruning, are gradually overcoming this challenge. Today, AI no longer relies entirely on the cloud and can operate on smaller, low-power devices, making Edge AI applications more practical and widely accessible—from industrial sites to personal gadgets.

According to IDC, global spending on edge computing solutions is expected to reach $261 billion in 2025 and grow at a compound annual growth rate (CAGR) of 13.8%, surpassing $380 billion by 2028. AI is one of the main drivers of this growth. Edge AI offers several key advantages:

．Real-time response and low latency:
Edge AI processes data locally without waiting for cloud responses. This is crucial for applications like connected vehicles or wearable devices, where immediate decisions improve speed and user experience.

．Reduced bandwidth and cloud costs:
Local data processing minimizes large uploads and cloud expenses while lowering network congestion, which is especially beneficial for AIoT devices.

．Improved Operational Efficiency:
Edge AI can automatically analyze data and make real-time decisions, handling most monitoring and management tasks on its own. This allows businesses to maintain high operational efficiency with fewer human resources.

．Enhanced security and privacy:
Data is processed and stored locally, reducing the risk of leaks during transmission. For industries like defense, finance and healthcare, this approach ensures better regulatory compliance and privacy protection.

．Transportation:
Connected vehicles use Edge AI to recognize pedestrians, other vehicles, and weather changes in real time, allowing for safe and responsive navigation.

．Energy:
In power grids, Edge AI integrates historical data and equipment status to predict demand, detect anomalies, and automatically adjust distribution. Smart grids become more stable and efficient, reducing the risk of outages.

．Manufacturing:
Edge AI monitors factory equipment in real time, predicts potential failures, and issues maintenance alerts in advance, reducing downtime and maintenance costs while optimizing production quality.

．Retail:
Edge AI analyzes sensor and camera data to track customer behavior and product movement, enabling cashier-less checkout, smart restocking, and real-time inventory management, improving both efficiency and customer experience.

．Healthcare:
Edge AI can process patient data locally on hospital devices, such as medical imaging or physiological monitoring data, without sending sensitive information to the cloud. This not only speeds up analysis but also ensures patient privacy and data security.

GIGABYTE helps businesses unlock the full potential of data at the edge and drive AI innovation. Together with its subsidiary GIGAIPC, GIGABYTE offers a range of edge computing products, including E-Series edge servers, industrial PCs, embedded systems, and e-Mobility platforms for robots and autonomous vehicles.

GIGABYTE’s edge servers strike a balance between memory, storage, and scalability, supporting PCIe Gen 5.0 and AI accelerator cards to run AI workloads like computer vision and speech recognition. Additionally, embedded systems based on NVIDIA® Jetson Orin™ and Thor™ power industrial automation, intelligent surveillance, and robotics worldwide.