Edge AI Terminals (with Jetson) and General-Purpose PCs (with RTX) — How They Differ and Why to Use Them

Edge AI terminals and general-purpose PCs (desktop/laptop PCs) are both broadly considered "computers." However, their design philosophies, use environments, and specialized applications differ greatly. In this article, we will organize the differences (suitability and unsuitability) using NVIDIA's "Jetson" and "RTX" commonly cited in video AI, and explain why "Edge AI terminals" are chosen.

Table of Contents

1. First, Overview: Edge AI Terminals as a Type of PC

2. Differences Between Jetson and RTX (suitability and unsuitability)

3. What General-Purpose PCs with RTX Excel At (and Price Range)

4. Reasons Why Edge AI Terminals Are Still Needed

5. Challenges When Using RTX-equipped PCs as Substitutes

6. Reasons Why Zenmov × EDGEMATRIX Can Be Said to Make Operations Easy

7. Conclusion

Additional Note: Why NVIDIA is Commonly Used in Edge AI (CUDA Ecosystem/CUDA Tile)

1. First, Overview: Edge AI Terminals as a Type of PC

Edge AI terminals (edge PCs, industrial AI terminals, etc.) share the basic structure of computers with general-purpose PCs in terms of input (camera images/sensors) → processing (AI inference) → output (notifications/control/storage). However, generally, when people refer to "PCs," they often mean desktop or laptop PCs used in offices or homes, so in this article, we will distinguish between "general-purpose PCs (with RTX)" and "edge AI terminals (with Jetson)."

In simple terms,

• General-Purpose PCs (with RTX): High-performance general PCs designed for a wide range of uses, intended to operate in a stable indoor environment.

• Edge AI Terminals (with Jetson): Application-specific PCs optimized for on-site use.

2. Differences Between Jetson and RTX

Here, we will focus on NVIDIA products commonly mentioned in video analysis contexts. Jetson is a "compact computer intended to be placed on-site," while RTX refers to "high-performance GPU cards inserted into PCs and servers."

Jetson (For Edge)

• An edge-oriented platform integrated with ARM CPU and GPU

• Emphasis on compactness, low power consumption, and continuous operation (designed for on-site installation)

• Easily completes real-time inference on-site in combination with cameras and sensors.

• Often embedded in devices that have environmental resistance (dustproof, vibration-resistant, etc.)

RTX (For General-Purpose PCs/Servers)

• Discrete GPUs (cards) that are primarily installed in PCs/servers

• Overwhelming GPU performance and price competitiveness are appealing (applications are wide-ranging)

• Can accommodate "general purposes" like PC work, development environments, verification, etc.

• However, the installation environment is generally indoors (requiring power, air conditioning, and chassis space)

3. What General-Purpose PCs with RTX Excel At (and Price Range)

In conclusion, general-purpose PCs with RTX are very attractive in terms of "performance" and "price." Especially in environments where PoCs (proof of concepts) are operated and where cooling is adequate (such as server rooms and offices), they represent a realistic option.

• Performance: High GPU performance makes it easy to cover a wide range from video analysis to large model verification

• Flexibility: With general-purpose OS (Windows/Linux, etc.), software can be freely configured based on application needs

• Cost: When aiming for comparable inference performance, there can be “cost-effective” cases depending on configuration

• Typical PC tasks (development, editing, analysis, etc.) can also be handled on the same device.

〈Price Range Guide〉※As of 2025

Prices can vary significantly based on GPU generation, CPU, memory, chassis (industrial or not), and acquisition channels (retail/BTO/business-oriented). For reference, here’s a summary of the price range commonly seen in the domestic market.

It is not uncommon for "RTX-equipped PCs to appear to be higher performance and lower cost." Nevertheless, the reason edge AI terminals are chosen is largely due to the operational conditions discussed in the next chapter.

4. Reasons Why Edge AI Terminals Are Still Needed

Running AI is possible even with a general-purpose PC. For instance, detecting something as simple as "a car is present" or "a person has passed" when reading camera footage can be done with an office PC.

On the other hand, edge AI truly demonstrates its value when the following on-site conditions are met.

• Installed in “on-site” locations like parking lots, factories, stores, roads, and facility entrances.

• Required to operate continuously 24 hours a day, 365 days a year without stopping.

• Environmental conditions are harsh, such as heat, cold, dust, and vibration.

• Constraints exist for power supply and installation space (energy-efficient and compact design is crucial).

To meet these requirements, edge AI terminals (edge PCs) have been developed. While edge terminals are also PCs, they specialize in the parts commonly used in AI (such as GPUs) while minimizing elements that can become unnecessary on-site, thereby enhancing the cost-performance and operability of continuous inference.

Next, by comparing Jetson and RTX, the differences become clearer.

5. Challenges When Using RTX-equipped PCs as Substitutes

The thought of simply placing an RTX-equipped general-purpose PC instead of Jetson on-site may be natural. However, seeking to meet the “on-site requirements” that edge AI arouses usually leads to the following challenges.

① Increased Operational Costs and Inefficiency

• Electricity costs tend to rise (the impact increases with the number of units)

• High heat generation increases the need for cooling, dust prevention, and noise control

• In locations like outdoors or in vehicles, where there are restrictions on power supply and space, installation may become difficult.

② Difficulty in Ensuring Real-Time Performance and Reliability

• Dust, vibrations, and temperature changes increase the risk of failures (stopping can cause issues on-site)

• There is a need to design and monitor for continuous operation, including OS updates and reboots, in-house

• Maintenance design increases with site-specific wiring, installation, remote monitoring, disaster recovery procedures, etc.

In summary, while RTX-equipped PCs possess high processing performance, ensuring continuous operation 24/7 on-site is challenging. Furthermore, the need to build everything from operational design to maintenance and upkeep from scratch tends to create a heavy burden. The value of edge AI terminals lies in their ability to absorb this burden as a “product,” simplifying on-site installations.

6. Reasons Why Zenmov × EDGEMATRIX Can Be Said to Make Operations Easy

At this point, your question of "Why are edge terminals necessary despite RTX often being high-performance and low-cost?" has likely become more specific. When considering the operational aspects, edge AI terminals are "PCs packaged with the premise of on-site AI operation," significantly reducing post-introduction burdens.

With the Jetson-based edge AI terminals that Zenmov offers, the following management can be conducted primarily through a dashboard.

• Unified terminal management: Terminal listings, operational states (online/offline), health checks such as temperature and resource usage rates.

• Distribution of AI applications/models: Implementing installation and updates of inference apps, configuration distribution, reboots, etc. remotely.

• Camera/input management: Stream settings, configuration of analysis target areas (ROI) and detection conditions.

• Alerts/logs: History of detection events, log collection, notification settings for alerts.

• Operational efficiency: Standardizing procedures that tend to become disparate by site and facilitating maintenance and recovery.

While it is possible to aim for equivalent operation with general-purpose PCs (with RTX), there is a need to "design" monitoring, updates, log collection, and recovery during failures in-house, and as the scale increases, the operational burden becomes more pronounced.

7. Conclusion

RTX-equipped general-purpose PCs strike an excellent balance between performance and price, making them very effective for PoC and indoor operations. In contrast, edge AI terminals are designed with premises of "low power consumption, environment resistance, continuous operation, and remote operation," and provide overall peace of mind and reduced operational burden in applications that require continuous operation on-site.

• PoC and indoor verification: RTX-equipped PCs are strong contenders (high performance, flexibility, cost-effectiveness)

• Continuous operation on-site: Edge AI terminals with Jetson are excellent (low power consumption, environmental resilience, standardization of operations).

Additional Note: Why NVIDIA is Commonly Used in Edge AI (CUDA Ecosystem)

The background for the frequent use of NVIDIA products in the edge AI field lies in the powerful support for development provided by the "CUDA Ecosystem (development infrastructure)" in addition to hardware performance.

• CUDA Ecosystem: The parallel computing infrastructure "CUDA" is widely used as a de facto standard in the AI/deep learning field.

• Compatibility with major frameworks: Major frameworks such as TensorFlow and PyTorch are optimized for NVIDIA GPUs, facilitating the transition from training to inference.

• Rich libraries/SDKs: Abundant application-specific SDKs for image recognition, video processing, inference optimization, robotics, etc., lead to reduced development time and costs.

As a result, it becomes easier to connect training (server side) and inference (edge side) with the same NVIDIA stack, which also generates the advantage of improving overall operational visibility.

Moreover, in recent years, there has been a movement to make CUDA more accessible to Python users with the introduction of "CUDA Tile (tile-based programming model)" and "cuTile Python" as part of CUDA Toolkit 13.1.