RK3588 · NVIDIA Jetson Orin · RTX MXM

Industrial Edge AI Computers for Machine Vision, Robotics & Local AI

RK3588, NVIDIA Jetson Orin and x86 + RTX MXM platforms for machine vision, robotics, video analytics and local AI — with industrial I/O, wide-range DC power options, lifecycle support and OEM/ODM customization.

RK3588

Low-power NPU for gateways, lightweight vision and embedded AI.

NVIDIA Jetson Orin

CUDA / TensorRT for multi-camera vision, robotics and autonomy.

x86 + RTX MXM

Discrete GPU for AOI, local LLM and Windows/Linux AI workloads.

Choose an Edge AI Computer by Workload

Start from workload, not TOPS. Match the model, software stack, camera topology and sustained thermal requirement together.

WorkloadRecommended Platform / ModelWhy
AI gateway / 1–2 cameras / lightweight inferenceAE-3588NBT / AE-3588LBTLow-power RK3588 platform for embedded inference
Factory AI with isolated field interfacesAE-3588BTIsolation options for CAN, serial and GPIO integration
Multi-camera vision / GMSL2 / roboticsAE-NJ60BTJetson CUDA / TensorRT with camera connectivity
Heavy AOI / Windows AI / local LLMAX-760EBTx86 software compatibility with RTX MXM acceleration
Sealed or airflow-constrained high-GPU systemAE-960EBTCPU/GPU cold plates move heat to a remote radiator

Edge AI Computer Models

Compare the platform details that determine deployment fit. Confirm final interfaces and environmental limits on each product page.

AE-3588NBT RK3588 fanless industrial edge AI computer

Rockchip RK3588

AE-3588NBT

AI:  Integrated 6 TOPS NPU
Cooling: Fanless passive cooling
Camera / Network: MIPI / HDMI input / GbE
Industrial I/O: RS-232 
OS: Linux / Android
Best fit: AI gateway and lightweight vision

AE-3588LBT RK3588 industrial edge AI computer with multi-LAN

Rockchip RK3588

AE-3588LBT

AI: Integrated NPU + accelerator option
Cooling: Fanless passive cooling
Camera / Network: Multi-LAN / MIPI / USB
Industrial I/O: CAN / serial I/O / GPIO
OSLinux
Best fit: AOI, AMR and distributed vision

AE-3588BT RK3588 industrial edge AI computer with isolated CAN and RS485Rockchip RK3588J

AE-3588BT

AI: Integrated NPU + accelerator option
Cooling: Fanless passive cooling
Camera / Network: Multi-LAN / MIPI / USB
Industrial I/O: Isolated CAN / serial / GPIO
OS: Linux
Best fit: Factory AI, energy and field systems

AE-NJ60BT NVIDIA Jetson Orin industrial edge AI computer for multi-camera visionNVIDIA Jetson Orin NX

AE-NJ60BT

AI: CUDA / Tensor Core
Cooling: Fanless passive cooling
Camera / Network: GMSL2 / MIPI / 4× GbE
Industrial I/O: CAN FD / DIO
OS: Ubuntu / JetPack
Best fit: Multi-camera vision and robotics


AE-760EBT Intel Core and RTX MXM industrial edge AI computer

Intel Core + NVIDIA RTX MXM

AX-760EBT

AI: Discrete RTX GPU
Cooling: Active industrial air cooling
Camera / Network: Multi-LAN / PCIe expansion
Industrial I/O: CAN / COM / isolated DIO
OS: Windows / Linux
Best fit: AOI, local LLM and GPU inference

AE-960EBT liquid-cooled RTX MXM industrial edge AI computer with remote radiator

Intel Core + NVIDIA RTX MXM

AE-960EBT

AI: RTX 5080-class launch target
Cooling: Liquid cold plates + remote radiator
Camera / Network: Project-specific
Industrial I/O: Project-specific
OS: Windows / Linux
Best fit: Sealed, airflow-constrained high-GPU systems

Compare BITECH Edge AI Computers

ModelComputeCoolingCamera / NetworkIndustrial I/OOSBest Fit
AE-3588NBTRockchip RK3588Fanless passive coolingHDMI input / GbERS-232 / RS-485Linux / AndroidAI gateway and lightweight vision
AE-3588LBTRockchip RK3588Fanless passive coolingMulti-LAN / MIPI / USBCAN / serial I/OLinuxAOI, AMR and distributed vision
AE-3588BTRockchip RK3588JFanless passive coolingMulti-LAN / MIPI / USBIsolated CAN / serial / GPIOLinuxFactory AI, energy and field systems
AE-NJ60BTNVIDIA Jetson Orin NXFanless passive coolingGMSL2 / MIPI / 4× GbECAN FD / DIOUbuntu / JetPackMulti-camera vision and robotics
AE-760EBTIntel Core + NVIDIA RTX MXMActive industrial air coolingMulti-LAN / PCIe expansionCAN / COM / isolated DIOWindows / LinuxAOI, local LLM and GPU inference
AE-960EBTIntel Core + NVIDIA RTX MXMLiquid cold plates + remote radiatorProject-specificProject-specificWindows / LinuxSealed, airflow-constrained high-GPU systems

Final interfaces, accelerator configuration, environmental limits and software support are confirmed against the selected product specification and project configuration.

RK3588 vs Jetson Orin vs x86 + RTX

Three AI compute classes, each with a different software, camera and thermal profile. Select by workload and sustained performance — not TOPS alone.

/engineering/jetson-orin-vs-rk3588-vs-x86
CategoryRK3588Jetson Orinx86 + RTX
Compute classIntegrated NPUCUDA / Tensor CoreDiscrete GPU
SoftwareRKNNJetPack / CUDA / TensorRTCUDA / ONNX / Windows / Linux
Power classLowMediumHigh
Camera architectureMIPI / USB / EthernetGMSL2 / MIPI / EthernetGigE / USB3 / PCIe frame grabber
Best workloadGateway / light visionRobotics / multi-cameraAOI / LLM / heavy AI
Read the full platform selection guide

What Makes a BITECH Edge AI Computer Industrial?

Industrial readiness is established through the selected electrical, thermal, validation and lifecycle configuration—not through a single feature claim.

Electrical

  • —Wide-range DC power options
  • —CAN / RS-485 / GPIO
  • —Optional galvanic isolation
  • —Platform-specific transient design
Review isolation design →

Thermal

  • —Fanless passive cooling
  • —Active industrial air cooling
  • —Liquid cold-plate architecture
  • —Sustained workload design
Review sustained thermals →

Validation

  • —Board-level testing
  • —Complete-system testing
  • —Burn-in and interface verification
  • —Serial-number traceability
View validation process →

Thermal Architectures for Sustained AI Workloads

Fanless Passive Cooling

For low-to-medium power RK3588 and selected Jetson platforms.

Active Industrial Air Cooling

For high-power x86 + RTX MXM systems requiring sustained GPU performance.

Liquid Cold-Plate Cooling

For airflow-constrained systems where CPU/GPU heat moves to a remote radiator.

AE-960EBT: the compute chassis uses liquid cold plates on the CPU and MXM GPU. Coolant transports heat to an external radiator, where external fans dissipate it.

Edge AI Computer vs Standard Industrial PC

FeatureEdge AI ComputerStandard Industrial PC
AI accelerationNPU / GPUCPU / iGPU
Main workloadSustained inferenceControl / HMI / gateway
Vision interfaceCamera-orientedGeneral-purpose I/O
AI softwareRKNN / JetPack / CUDAWindows / Linux applications
Typical useVision / robotics / local AIAutomation / control / data acquisition
Need a CPU-only industrial computer? View Industrial Box PCs

OEM / ODM Engineering

Configure the Platform Around Your Deployment

BITECH can review custom I/O, camera topology, enclosure, mounting, system image and lifecycle requirements after the workload and program constraints are defined.

Review OEM / ODM engineering

Define before customization

  • AI model and framework
  • Camera count and bandwidth
  • Field I/O and isolation
  • DC input and transients
  • Thermal environment
  • Mounting and enclosure
  • System image
  • Annual volume

Talk to an engineer

NPU, Jetson or RTX GPU? Size the platform from your workload

Most edge AI projects are oversized or undersized because the platform is chosen before the workload is defined. Send us your models, cameras and site conditions — a hardware engineer will recommend the smallest BITECH platform that runs them reliably, and tell you when a CPU-only industrial PC is enough.

Send us these and we can recommend a platform

  • AI workloadModels, input resolution, target FPS and latency — and whether the stack needs CUDA / TensorRT, RKNN or OpenVINO
  • CamerasNumber of cameras and interface: GMSL2, MIPI, GigE Vision, USB3 or HDMI capture
  • Field I/OCAN / CAN FD, RS-232/485, DIO and whether devices share a ground or need isolation
  • Power & thermalDC supply or battery voltage, ambient range, enclosure airflow, dust and vibration
  • DeploymentAMR / AGV, AOI cell, energy cabinet, vehicle or outdoor site — plus 4G/5G or Wi-Fi needs
  • VolumePilot quantity, annual volume, lifecycle target and any carrier board, enclosure or OS customization

An engineer — not a sales bot — replies within 24 hours. Sample units, thermal test data and 18-hour burn-in records available on request.

No AI model, just control, HMI or networking? A CPU-only platform is usually cheaper — view industrial box PCs →

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.

Related Engineering Resources

The design questions behind choosing an edge-AI platform — thermal path, isolation, lifecycle, and validation.

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