Edge AI Terminals (Equipped with Jetson) and General-Purpose PCs (Equipped with RTX) — What Are the Differences and Why Use Them Separately?

Edge AI terminals and general-purpose PCs (desktop PCs/laptop PCs) are both broadly defined as "computers". However, their design philosophies, the environments in which they are used, and their areas of expertise differ significantly. This article will clarify the differences (suitability) using NVIDIA's "Jetson" and "RTX" as examples, while explaining why "Edge AI terminals" are chosen.

Table of Contents

1. First, let's clarify: Edge AI terminals are also a type of "PC".

2. Differences between Jetson and RTX (suitability)

3. Strengths of general-purpose PCs equipped with RTX (+ price range)

4. Reasons why Edge AI terminals are still necessary

5. Challenges that may arise when using RTX-equipped PCs as substitutes

6. Reasons why Zenmov × EDGEMATRIX can claim "easy operation".

7. Summary

Additional Notes: Why NVIDIA is Often Used in Edge AI (CUDA Ecosystem / CUDA Tile)

1. First, let's clarify: Edge AI terminals are also a type of "PC".

Edge AI terminals (Edge PCs, industrial AI terminals, etc.) have the same basic structure as general-purpose PCs in terms of input (camera footage/sensors) → processing (AI inference) → output (notifications/control/storage). However, the term "PC" generally refers to desktop PCs or laptops used in offices or homes, so in this article, we will differentiate by referring to them as "general-purpose PCs (RTX-equipped PCs)" and "edge AI terminals (equipped with Jetson)".

To summarize briefly,

• General-Purpose PCs (RTX-equipped PCs): High-performance general PCs capable of handling a wide range of applications designed for stable indoor environments.

• Edge AI Terminals (Jetson-equipped terminals): “Application-specific PCs” optimized for field use.

2. Differences between Jetson and RTX

Here, we will discuss NVIDIA products frequently mentioned in the context of video analysis. Jetson is a "compact computer intended for deployment in the field," while RTX is a "high-performance GPU card to be inserted into PCs or servers."

Jetson (for edge)

• An edge-focused platform integrating ARM CPU and GPU.

• Prioritizes compact size, energy efficiency, and continuous operation (designed for field deployment).

• Easily combines with cameras and sensors, facilitating real-time inference on-site.

• Often integrated into terminals with environmental resilience (dustproof, vibration-resistant, etc.).

RTX (for general-purpose PCs/servers)

• Mainly a discrete GPU (card) used in PCs/servers.

• Outstanding GPU performance and cost competitiveness are appealing (wide range of applications).

• Can comfortably accommodate "general-purpose applications" such as PC tasks, development environments, and verification.

• However, the installation environment is fundamentally indoor (requires power supply, air conditioning, and casing space).

3. Strengths of General-Purpose PCs Equipped with RTX (+ Price Range)

In conclusion, general-purpose PCs equipped with RTX are very attractive in terms of "performance" and "price." Especially in environments suitable for PoC (proof of concept) and operation in air-conditioned server rooms or offices, they become a realistic choice.

• Performance: High GPU performance makes it easier to address a wide range, from video analysis to large model validation.

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

• Cost: Depending on the configuration, there are cases where it can be "cost-effective" if aiming for comparable inference performance.

• Can handle regular PC tasks (development, editing, analysis, etc.) on the same device.

〈Price Range Estimate〉 ※ As of 2025

Prices vary significantly based on GPU generation, CPU and memory, casing (industrial or not), and acquisition route (mass-market/BTO/business-use). Here, we will roughly organize the ranges encountered in the domestic market.

It is not uncommon that "RTX-equipped PCs appear to be higher performance and cheaper." Nevertheless, edge AI terminals are chosen because the “operational conditions” in the next chapter have a significant influence.

4. Reasons Why Edge AI Terminals Are Still Necessary

In cases where you only need to "run AI," general-purpose PCs are sufficient. For instance, detecting "a car is visible" or "a person passed by" can be processed on an office PC.

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

• Installed in "settings" like parking lots, factories, stores, roads, and entrances to facilities.

• Must continue operating without stopping, 24 hours a day, 365 days a year.

• Harsh environments with heat, cold, dust, vibration, etc.

• Constraints on power supply or installation space (compact and energy-efficient is crucial).

The edge AI terminal (edge PC) was created to meet these requirements. While edge terminals are also PCs, by specializing in the parts frequently used for AI (like GPUs) and minimizing elements that become unnecessary in the field, they enhance the cost performance and usability of "continuing to run inference."

Next, comparing Jetson and RTX will clarify the differences even more.

5. Challenges Likely to Arise When Using RTX-Equipped PCs as Substitutes

The idea of replacing Jetson with RTX-equipped general-purpose PCs at the site is natural. However, if one tries to meet the “field requirements” that edge AI must fulfill, the following challenges are likely to emerge.

As illustrated above, while RTX-equipped PCs boast high processing performance, it is difficult to maintain continuous 24/7 operation in the field. Additionally, designing from scratch for operations, maintenance, and upkeep can become a heavy burden. The value of edge AI terminals lies in their ability to absorb such burdens “as a product” and facilitate field introductions.

6. Reasons Why Zenmov × EDGEMATRIX Can Claim "Easy Operation"

By now, the question "Why are edge terminals necessary when RTX is often high-performance and cheap?" likely has become clearer. Considering aspects of operations, edge AI terminals are "PCs packaged for AI field operations," significantly altering the burden after installation.

In edge AI terminals based on Jetson handled by Zenmov, the following management tasks can be conducted mainly through a dashboard.

• Centralized management of terminals: terminal list, operational status (online/offline), health checks like temperature and resource usage.

• Distribution of AI applications/models: Remote implementation of inference app installation/updates, configuration distribution, restarts, etc.

• Management of cameras/inputs: Stream settings, target area of analysis (ROI), and detection condition settings.

• Alerts/logs: History of detection events, log collection, and alert notification settings.

• Streamlining operations: Standardizing procedures that tend to vary across sites, making maintenance and recovery easier.

While it is also possible to aim for similar operations with general-purpose PCs (equipped with RTX), it requires designing "in-house" for monitoring, updating, log collection, and recovery from failures, which increases the operational burden as the scale becomes larger.

7. Summary

General-purpose PCs equipped with RTX offer an excellent balance of performance and price, making them very attractive for PoC or indoor operations. On the other hand, edge AI terminals are designed with "energy efficiency, environmental durability, continuous operation, and remote operation" in mind, providing overall peace of mind and reducing operational burdens for applications that need to "continue running without stopping" in the field.

• PoC and indoor verification: RTX-equipped PCs are promising (high performance, flexible, cost-effective).

• Continuous operation in the field: Edge AI terminals equipped with Jetson are promising (energy-efficient, environment-resistant, standardized operation).

Additional Notes: Why NVIDIA is Frequently Used in Edge AI (CUDA Ecosystem)

The background behind the frequent use of NVIDIA products in the edge AI field includes not only hardware performance but also the well-established "CUDA ecosystem (development foundation)" that robustly supports development.

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

• Compatibility with major frameworks: Major frameworks like TensorFlow and PyTorch are optimized for NVIDIA GPUs, making transitions from training to inference easier.

• Rich libraries/SDKs: There are abundant application-specific SDKs for image recognition, video processing, inference optimization, robotics, etc., leading to reductions in development time and costs.

As a result, it becomes easier to connect training (server side) and inference (edge side) within the same NVIDIA ecosystem, creating an advantage of clearer forecasts for overall operations.

Recently, developments are also underway to make CUDA more user-friendly for Python users with the introduction of "CUDA Tile (tile-based programming model)" and "cuTile Python" in CUDA Toolkit 13.1.