AI edge technology enhances the efficiency of limited resource networks, enabling them to perform complex tasks locally, reducing network traffic and latency.
In a groundbreaking collaboration, Broadcom and Beecham Research have delved into the rapidly expanding world of edge AI, exploring its potential for real-time decision-making and predictive analysis in enterprise Wide Area Networks (WAN).
Edge AI, powered by tools like Latency-aware Machine Learning (LLM) and Scalability-aware Machine Learning (SLM), is becoming a cornerstone of Industry 4.0 technology and latency-sensitive applications. Key drivers for this adoption include Industry 4.0, scalability, cost considerations, and compliance & security concerns.
The popularity of edge AI is on the rise, with AI advancements playing a significant role in its growing appeal. The global market size for edge AI in enterprise WAN is estimated to reach an impressive $20 billion by 2024, with an annual growth of approximately 25%.
This deep dive into edge AI for enterprise resource-constrained distributed networks also highlights the challenges in implementing, managing, and scaling edge AI solutions. Security concerns and network issues such as bandwidth, reliability, power, and data requirements are key considerations. However, resource management and security are also key benefits provided by AI in this context.
Broadcom has introduced VeloRAIN (VeloCloud Robust Artificial Intelligence Networking) to address these challenges. This innovative product is designed to balance the performance and complexity of new AI models through efficient encoding, model cooperation, prioritization, edge storage, and dynamic workload distribution.
The report also features edge AI for enterprise resource-constrained distributed networks, showcasing how developments in chipsets and edge hardware are enabling higher data loads and low-latency computing at scale.
As for the current state, enterprises are increasingly adopting edge AI to leverage the benefits of low latency and real-time decision-making, particularly in industries like manufacturing and smart cities. Europe, Asia-Pacific, and North America are expected to lead in terms of market maturity, while Asia-Pacific is likely to experience rapid growth due to the increasing adoption of 5G and IoT technologies.
Looking ahead, the global edge AI market is projected to reach $62.93 billion by 2030, with a Compound Annual Growth Rate (CAGR) of 20.1%. The edge AI hardware market specifically is expected to grow from $26.14 billion in 2025 to $58.90 billion by 2030, at a CAGR of 17.6%.
Regional growth drivers include Europe, expected to benefit from significant investments in 5G infrastructure and industrial IoT applications; Asia-Pacific, rapidly expanding due to the large-scale adoption of IoT and 5G technologies in countries like China and Japan; and North America, driven by advancements in edge computing for smart cities and industrial automation.
Key trends include the integration of edge AI with 5G networks for low-latency and high-bandwidth requirements of enterprise WAN applications, and the use of AI in security service edge (SSE) solutions to enhance network security across distributed environments.
In conclusion, the edge AI market for enterprise WAN is poised for substantial growth, driven by technological advancements, strategic partnerships, and increasing demand for real-time data processing and security solutions.
Artificial-intelligence technologies, such as Latency-aware Machine Learning (LLM) and Scalability-aware Machine Learning (SLM), are playing a significant role in the growing appeal of edge AI, which is becoming a cornerstone of Industry 4.0 and latency-sensitive applications. In response to the challenges in implementing and managing edge AI solutions, Broadcom has introduced VeloRAIN, an innovative product designed to balance the performance and complexity of new AI models.
