In recent years, AI computing has mostly relied on the cloud. However, as usage grows, companies face critical decisions regarding data privacy, latency, and operational costs. Moving to "Edge AI" (on-site computing) is no longer just an option—it is a must for digital transformation.
While bringing AI to the edge sounds perfect in theory, the reality is full of challenges. From a management perspective, building an Edge AI infrastructure often brings three hidden obstacles. Here are the operational pain points I have observed on the front lines, and how our team is helping businesses solve them.
Deploying Edge AI—such as traffic analysis in smart cities or optical inspection (AOI) in automated factories—means generating massive amounts of data on-site. Under a traditional setup, sending all raw data back to a central cloud server creates a bandwidth disaster and leads to uncontrollable data transfer costs. For small and medium enterprises (SMEs), building large on-premise data centers is simply too expensive.
As leaders, our goal should be: How can we upgrade our existing systems most cost-effectively?
Our ViClaw Edge AI + Storage Solution, developed in partnership with DEEPX, was created to break this deadlock. This add-in card delivers up to 50 TOPS of AI computing power and uses the concept of "Computational Storage," which means the AI analysis happens right where the data is stored.
Once raw images are saved locally, they are analyzed immediately. The system only uploads events, alerts, or statistical summaries. This architecture minimizes bandwidth needs and allows companies to instantly upgrade their existing equipment with strong AI computing power, balancing performance and capital expenditure (CAPEX).
Edge devices are rarely kept in temperature-controlled server rooms. Instead, they sit next to factory production lines, inside streetlights, in vehicles, or in outdoor cabinets. To protect them from dust and oil, these devices are usually designed with fanless, sealed enclosures that rely entirely on passive cooling.
This creates a major operational contradiction: The places that need high-performance computing the most are the exact places where we cannot use active cooling fans.
For example, Edge AI video analysis runs 24/7. When high-density data reading and writing meet thin, sealed, fanless devices, a "thermal storm" occurs. If storage components overheat and slow down, it causes AI analysis delays, drops in accuracy, and even data corruption or system downtime. These are operational risks businesses cannot afford.
Therefore, a component's ability to cool itself is a key metric when choosing Edge AI hardware. We have long focused on industrial cooling technology. Our GraTherXTM DDR5 Thermal Solution and CoreGlacier 2 cooling solutions use advanced materials and structures to solve overheating right at the source, ensuring your AI stays online 24/7 without changing your equipment's exterior design.
Keeping data at the edge solves privacy and delay issues, but it creates a management side effect: when a company has hundreds of scattered edge sites, protecting data and recovering from disasters becomes highly time-consuming and expensive. If an IT person has to visit the site every time a device fails, the system loses its business value.
To make a "data stays on-site" architecture truly practical for business, you need strong remote maintenance support. Our CoreSnapshot 2 technology is designed precisely to solve this problem for distributed edge devices. It features remote backup and one-second recovery. If an off-site device experiences a system error, the management team can restore data and systems instantly from afar. This minimizes downtime risks and significantly reduces labor costs.
Accelerating Business AI Success with Strategic Infrastructure
The success of Edge AI is ultimately a business race about efficiency, cost, and risk management. When planning your strategy, leaders should look beyond just raw computing power. We must also evaluate if the architecture is flexible, the hardware is durable, and the maintenance is simple.
We are dedicated to building a solid foundation for enterprises—from storage and computing power to maintenance—helping you move faster and further in your AI journey.
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