Edge AI Industrial Computers
Industrial-Grade AI Platforms for Real-Time Inference at the Edge
Edge AI computers are designed for real-time AI inference close to machines and sensors—where latency, reliability, and data privacy matter. Built for continuous operation in industrial environments with fanless thermal design, isolated I/O, and validated NPU performance.
Scalable Edge AI Platforms: RK3588, NVIDIA Jetson & RTX GPU MXM series

AE-3588BT
58 TOPS Scalable Industrial Edge AI Computer for Energy & Semiconductor
- Up to 58 TOPS (6 onboard + 52 expansion)
- 4000V isolated CAN/RS232/RS485/GPIO
- Wide temp: -40°C to +80°C

AE-3588LBT
32 TOPS Scalable Edge AI for AOI, AGV/AMR & Smart City
- Up to 32 TOPS (6 onboard + 26 expansion)
- Non-isolated low-latency fieldbus I/O
- Fanless design: -40°C to +80°C

AE-3588NBT
6 TOPS RK3588 Gateway for Vision Automation & Smart Infrastructure
- 6 TOPS onboard NPU, INT4/INT8/INT16/FP16
- HDMI 2.1 8K Out + HDMI 2.0 4K In
- Cost-effective: -10°C to +60°C

AE-NJ60BT
157 TOPS NVIDIA Jetson Orin NX Super Edge AI for Multi-Camera Vision & AMR
- Up to 157 TOPS (Jetson Orin NX Super 16GB)
- 4-lane MIPI CSI or 4× GMSL2 camera ingress
- 4× GbE + 2× CAN FD, -40°C to +70°C fanless

AE-760EBT
x86 Edge AI Box PC — Intel i9-13900 + NVIDIA RTX MXM Discrete GPU
- Up to 80+ TFLOPS (RTX 4060/4070/4090/5080/5090 MXM)
- 4× 2.5GbE + 2× CAN FD + isolated DIO/COM
- 9–36V wide voltage, dual 80 mm industrial fans
Edge AI Compute Platform Comparison: RK3588 vs NVIDIA Jetson Orin vs NVIDIA RTX MXM GPU
Three dominant silicon classes power industrial Edge AI today. The right choice depends on AI compute class (TOPS / TFLOPS), software ecosystem, power envelope, and operating temperature. Use this engineer-oriented matrix to match your inference workload to the correct platform before specifying I/O and chassis.
| Engineering Factor | Rockchip RK3588 / RK3588J (ARM) | NVIDIA Jetson Orin (ARM + GPU) | NVIDIA RTX MXM Discrete GPU (x86) |
|---|---|---|---|
| Architecture | 8-core ARM (4× A76 + 4× A55) + 6 TOPS NPU | ARM Cortex-A78AE + Ampere CUDA GPU + Tensor Cores | Intel Core i7/i9 (12th–14th Gen) + RTX 4060/4070/4090/5080/5090 MXM |
| AI Compute | 6 TOPS onboard, scalable to 32–58 TOPS via M.2 NPU | 40 / 100 / 157 / 275 TOPS (Orin Nano → AGX Orin 64GB) | Up to 80+ TFLOPS FP16 / 1300+ TOPS INT8 (RTX 5090 class) |
| Software Ecosystem | Linux / Android, RKNN Toolkit, ROS2, OpenCV | NVIDIA JetPack, CUDA, TensorRT, DeepStream, Isaac | Windows / Linux, full CUDA, TensorRT, PyTorch, Triton, LLM stacks |
| Typical Power | 8–25 W (fanless) | 15–60 W (fanless or low-noise) | 120–450 W (active cooling required) |
| Thermal Design | Fanless, -40°C to +80°C | Fanless, -40°C to +70°C | Industrial fans, -20°C to +60°C |
| Vision Ingress | MIPI CSI + USB cameras | MIPI CSI + GMSL2 (long-distance, EMI-resistant) | GigE Vision / 10GbE / USB3 Vision via PCIe |
| Best-Fit Workload | Edge gateway, lightweight vision, control + light AI, HMI | Multi-camera vision, AMR/AGV perception, autonomous driving | Generative AI, edge LLM, heavy multi-stream AOI, SCADA + AI |
| OS / Legacy Support | Linux / Android only | Ubuntu Linux (JetPack) | Windows 10/11 IoT, Linux — full legacy x86 compatibility |
| BITECH Models | AE-3588BT, AE-3588LBT, AE-3588NBT | AE-NJ60BT | AE-760EBT |
Choose RK3588 when
Cost, power budget, and long ARM Linux lifecycle dominate. Workload ≤ 32 TOPS — field gateways, control nodes, HMI + light vision.
Choose Jetson Orin when
Multi-camera vision, GMSL2 ingress, CUDA / TensorRT toolchain, and fanless wide-temp deployment for AMR / AGV / autonomous systems.
Choose RTX MXM GPU when
Workload requires Windows compatibility, edge LLM / generative AI, or 80+ TFLOPS for heavy multi-stream AOI and SCADA + AI convergence.
Why Industrial Environments Require Dedicated Edge AI Hardware?
Edge AI industrial computers are engineered for specific workloads—not generic compute.
Machine Vision & Inspection
Task: Run multi-camera defect detection at production speed
Why it matters: Inspection cycles require deterministic latency—cloud round-trips introduce unacceptable variance.
Platform benefit: Stable USB3/GigE bandwidth, NPU-optimized inference, and vision-ready I/O layout.
Real-Time AI Inference at the Edge
Task: Make decisions in milliseconds, not seconds
Why it matters: Robotics, AGVs, and safety systems cannot tolerate network delays or cloud outages.
Platform benefit: Sub-100ms inference latency with no dependency on external connectivity.
Isolated I/O and Fieldbus Integration (CAN, RS485)
Task: Connect AI to PLCs, sensors, and legacy equipment
Why it matters: AI systems must coexist with existing fieldbus infrastructure, not replace it.
Platform benefit: Isolated CAN, RS232/485, and industrial Ethernet for direct equipment integration.
Fanless Thermal Design for 24/7 Reliability
Task: Run inference workloads around the clock without degradation
Why it matters: Consumer hardware throttles under sustained load; production lines cannot afford downtime.
Platform benefit: Fanless thermal design validated for continuous AI inference at full load.
Long Lifecycle & Scalable Deployment
Task: Deploy the same platform across hundreds of sites
Why it matters: OEM projects require BOM stability and availability commitments, not consumer product cycles.
Platform benefit: 10 year availability, PCN/EOL management, and configuration freeze support.
Edge AI Industrial Computers vs Standard Industrial Box PCs
Edge AI Industrial Computers
- Designed specifically for AI inference workloads
- Validated thermal design for sustained NPU/GPU operation
- Vision- and sensor-optimized I/O layout
- Stable AI performance under continuous 24/7 load
- Pre-validated AI framework support (TensorFlow, ONNX, PyTorch)
Best for: Machine vision, inspection, robotics, edge analytics
Standard Industrial Box PCs
- General-purpose control and gateway tasks
- AI capability is optional or add-on based
- I/O optimized for automation, not vision
- Not validated for sustained inference workloads
- May require additional software integration
Best for: PLC control, data acquisition, industrial gateways
Typical Edge AI Applications
Automated Optical Inspection (AOI)
Real-time defect detection on production lines with multi-camera integration.
Robotics & Autonomous Systems
Low-latency perception and decision-making for AGVs and robotic arms.
Intelligent Transportation
Vehicle recognition, traffic analysis, and fleet monitoring at the edge.
Choosing an Edge AI Platform — FAQ
How to select between RK3588, Jetson Orin, and RTX MXM for your inference workload.
Match the silicon to your compute class and power budget. RK3588 (6–58 TOPS, 8–25W fanless) suits lightweight vision and cost-sensitive edge nodes. Jetson Orin (40–275 TOPS, 15–60W) fits multi-camera vision and AMR with the CUDA/TensorRT ecosystem. RTX MXM (80+ TFLOPS, 120–450W, active cooling) is for heavy inference, LLMs, and training-class workloads. Power envelope and operating temperature usually decide the boundary between them.
For AMR perception, the AE-NJ60BT (Jetson Orin NX, up to 157 TOPS) is the strongest fit — it offers MIPI CSI / GMSL2 camera ingress, 4x GbE, dual CAN FD, and -40C to +70C fanless operation. For lighter-cost AGV nodes, the AE-3588LBT (32 TOPS RK3588) provides low-latency fieldbus I/O at a lower price point. See our AMR controller solution →
For high-throughput multi-camera vision, the AE-NJ60BT (Jetson Orin) handles parallel camera streams with GMSL2/MIPI ingress and the DeepStream pipeline. For maximum inference performance — high-resolution AOI or model ensembles — the AE-760EBT (Intel i9 + RTX MXM) delivers GPU-class throughput. The AE-3588BT covers cost-sensitive single- or few-camera inspection.
All three are RK3588-based. The AE-3588BT scales to 58 TOPS and adds 4000V isolated CAN/serial/GPIO for harsh environments like energy and semiconductor. The AE-3588LBT scales to 32 TOPS with non-isolated low-latency I/O for AOI and AGV. The AE-3588NBT is a cost-effective 6 TOPS gateway with 8K HDMI out and 4K HDMI in for vision automation and infrastructure.
Choose a discrete RTX MXM GPU (the AE-760EBT) when you need the full CUDA/PyTorch/TensorRT stack, run large models or LLMs at the edge, or require training-class throughput. NPU platforms (RK3588, Jetson) are more power-efficient and fanless, but a discrete GPU delivers far higher raw compute when your workload demands it — at the cost of higher power and active cooling.
Yes. Beyond the standard models, BITECH offers custom baseboard design, enclosure sizing, and branded chassis modification (the AE-NY series) down to PCB level. This lets you match a specific compute module, camera interface, I/O layout, and form factor to your product, with batch deployment from low MOQ. See our OEM/ODM customization →
Yes, depending on the model. The AE-3588BT provides 4000V galvanic isolation on CAN, RS232/RS485, and GPIO for high-noise environments, and the AE-760EBT includes isolated DIO/COM. Isolation level and interface count can be specified at the project level. How interface isolation works →
Send your model type, target framerate, camera count and interface, power/thermal limits, and environment through our RFQ form. You talk directly to our hardware engineers, who will recommend the right silicon and configuration and arrange evaluation samples — typically within a 1–3 month cycle.
Not sure which Edge AI platform fits your workload?
Tell us your model, framerate, camera count, and power/thermal limits — our engineers will match you to the right silicon (RK3588, Jetson Orin, or RTX MXM) and quote evaluation samples. Need a fully custom baseboard or branded chassis? We design that too.
Custom baseboard design, enclosure sizing, and branded chassis available · Evaluation samples typically in 1–3 months