How to Implement 6TOPS Computing Power of RK3588: Baile Technology’s AXMxy BL450 Explains!

In recent years, whenever the topics of “AI edge inference”, “industrial vision”, and “local large models” come up, you must have heard the name RK3588. Its reputation is simple: 6TOPS computing power + strong video encoding/decoding + multiple interfaces, which can almost cover all scenarios of edge AI.

The Baile Technology BL450 is an industrial-grade AI edge computing gateway built on the RK3588 platform. Today, we won’t discuss concepts or pile up parameters; instead, we will address one question:

How to use the BL450 (RK3588)? What can it do? What configurations are needed?

Let’s get straight to the point.

1. BL450 = A Practical Industrial AI Host

First, let’s briefly understand this “little powerhouse”:

• Chip: Rockchip RK3588 (6TOPS NPU)

• System: Ubuntu / Debian / Buildroot

• Interfaces: RS485 / RS232 / CAN / 4G / Gigabit Ethernet / GPIO

• Video Capabilities: 4 channels of 4K cameras, 8K encoding/decoding

• AI Capabilities: Easily runs YOLO series, supports OpenVINO / RKNN

In summary:

It can handle almost all collection, recognition, inference, and gateway functions you can imagine in an industrial setting.

2. Four Typical Use Cases of BL450

1) Used as an “AI Camera Server” (Most Common)

Applicable for: Visual inspection, object recognition, behavior analysis, license plate recognition, etc.

Usage Steps:

1. Connect the camera (USB / MIPI / RTSP) to the BL450

2. Deploy inference models (YOLO, DeepSort, PP-YOLO, etc.) on the gateway

3. Use RKNN-Toolkit for model conversion

4. Start inference service and push recognition results via MQTT / Modbus / HTTP

5. Real-time reception by upper computer/SCADA/platform

You can think of it as: a local AI server that computes on-site without relying on the cloud.

2) Used as an “Edge Intelligent Gateway”

Applicable for: Energy storage, power, photovoltaics, factory production lines, unmanned equipment.

Usage Method:

1. BL450 collects data from on-site devices via multiple RS485/232 + Ethernet

2. Process logic using Node-RED / Python / C++

3. Local models for predictions:

• Temperature prediction

• Battery status prediction

• Device failure trend

Data sent back to the platform via MQTT/OPC-UA/Modbus-TCP

This is the “high-end flagship” of the AI version of BL118.

3) Used as a “Local Model Runner”

Many companies now want to deploy their large models:

• Small text models (3B / 7B quantized)

• OCR models

• Voiceprint recognition models

• Industrial defect models (glass, screws, solder joints)

• Travel wave recognition in power scenarios

The 6TOPS of RK3588 is significant in industrial settings:

You don’t have to upload data to the cloud, worry about latency, privacy, or bandwidth. Deploying a BL450 on-site allows the model to infer locally in a flash.

4) Used as a “Multi-Protocol Industrial Edge Server”

BL450 supports:

• Modbus RTU/TCP

• CAN

• OPC-UA

• IEC104

• MQTT

• BACnet

• Various proprietary protocols (via SDK for secondary development)

Typical Mode:

1. Use Python/C++ to parse on-site protocols

2. Write data to a local database (SQLite / Influx)

3. Then upload to the cloud platform / SCADA

Its positioning is not as a “cheap data pass-through”, but as a smart gateway capable of local strategies + local automation.

3. What Models Can Run on RK3588 (6TOPS) on BL450?

Below are the models that Baile Technology engineers have tested:

1) YOLOv5s / YOLOv8s / YOLOv10n: Smooth Operation

FPS can reach 20-40 (depending on resolution)

2) Human/Vehicle Detection and Tracking: Stable Operation

Most commonly used for:

• Factory personnel area breach

• Vehicle illegal parking

• Energy storage station safety detection

3) OCR / Text Detection

Such as:

• Device meter readings

• Label recognition

• Dashboard scale readings

4) Lightweight NLP Inference

The quantized version of Qwen 1.8B/3B can run as a local Q&A machine.

5) Multi-Camera Video Analysis

Supports:

• 2 channels of 4K

• or 4 channels of 1080P for simultaneous inference and display.

4. BL450 Usage Tutorial: From Power On to Running Models (Simplest Version)

Here is the most common and simplest entry process:

Step 1: Connect Devices

• HDMI screen / or SSH login

• Connect Ethernet cable

• Connect camera via USB/MIPI (if doing vision)

• Connect serial port to on-site devices (if doing collection)

Step 2: Install Dependencies (Baile provides SDK)

Common commands:

sudo apt update
sudo apt install python3-pip
pip3 install rknn-toolkit2

Step 3: Convert the Model to RKNN Format

On the PC:

from rknn.api import RKNN

Export the model as .rknn, then copy it to the BL450.

Step 4: Load the Model and Infer on BL450

from rknn.api import RKNN
rknn = RKNN()
rknn.load_rknn('model.rknn')
rknn.init_runtime()
outputs = rknn.inference(inputs=[img])

Step 5: Output Results to the Platform via Node-RED/MQTT

This is a major feature of Baile Technology’s BL series products:

• No need to write a lot of code

• Drag-and-drop logic

• Upload, alarm, and strategies are all completed locally

5. Real Industry Use Cases of BL450

Energy Storage Industry (Popular Scenarios for Baile)

• Abnormal temperature detection of battery packs

• Detection of personnel intrusion in energy storage cabins

• Smoke/flame visual detection

• EMS data collection + anomaly prediction

Photovoltaic Power Stations

• Component hot spot detection

• Local inference of inverters

• Edge AI detection of nests and obstructions

Factory Production Lines

• Defect detection

• Screw loosening

• Label recognition

• AGV local navigation inference

Smart Cities

• Parking management

• Flow statistics

• Local parsing by smart cameras

6. Conclusion: Why Recommend Using BL450 in Projects?

If I had to summarize in one sentence:

BL450 is an “industrial-grade device that can truly run AI on-site”, not a toy or a laboratory model.

The reasons boil down to three points:

1. Reliable performance with RK3588 + 6TOPS, no bottlenecks

2. Rich interfaces that can connect directly to industrial devices, not just cameras

3. Baile provides a complete SDK, Node-RED, and technical services for quick deployment