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AIO-8550JD4 AI Motherboard

48 TOPS Computing Power NPU

· AI Processor QCS8550  

· Deployment of Large AI Models

· Multiple Deep Learning Frameworks

· Supports Ray Tracing Technology  

· 8K Video Encoding and Decoding  

· Qualcomm Cognitive ISP with 36MP Triple Camera  

· Rich Expansion Interfaces

· Comprehensive Development Toolchain

Manual Wiki Download

Qwen

Gemma

Llama

DeepSeek

AIO-8550JD4
AIO-8550JD4 AI Motherboard

Equipped with the Qualcomm QCS8550 octa-core AI processor with a 48 TOPS NPU, it supports mainstream AI models and frameworks. It also features an Adreno 740 GPU for ray tracing and 8K video, three cognitive ISPs for up to 100MP cameras, and various expand ports. Technical support like AI optimization tools and reference designs enable efficient development.

AI Processor QCS8550

Features an octa-core Kryo CPU in the QCS8550, powered by the ARM architecture with a clock speed of up to 3.36 GHz, delivering robust support for high-performance computing and multitasking.

Private Deployment

Supports private deployment of large-scale Transformer models, including Qwen, Llama, Gemma, DeepSeek-R1, and Phi series.

Deep Learning Frameworks

Supports the QNN AI inference framework, as well as various deep learning frameworks including TensorFlow, TensorFlow Lite, PyTorch, and ONNX.

Ray Tracing Technology

Featuring the integrated Adreno 740 GPU, it fully supports ray tracing technology and is compatible with OpenGL ES 3.2, Vulkan 1.2, and the full profile of OpenCL 3.0. Coupled with the Hexagon direct neural acceleration, it delivers enhanced graphics processing and AI computing performance.

Suppurt 8K Video Codec

Supports advanced video processing with 8K@60fps/4K@240fps decoding and 8K@30fps/4K@120fps encoding, delivering exceptionally detailed and smooth ultra-high-definition visuals.

36MP Triple Camera

Features the Qualcomm Spectra Cognitive ISP with triple 18-bit ISPs, supporting camera configurations up to a triple 36MP, dual 64MP+36MP, or a single 108MP setup.

Rich Expansion Interfaces

Equipped with expansion interfaces such as MIPI-CSI, HDMI 2.0, RS485, RS232, CAN, I2C, UART, Type-C, USB3.0 and Mini PCIe, it meets the peripheral expansion needs of various scenarios.

Development Toolchain

A comprehensive suite is provided—including an AI model optimization tool, video processing SDK, and reference design with full documentation—to enable efficient secondary development and accelerate the creation of proprietary products.
48 TOPS NPU Computing Power

Integrated 48 TOPS NPU supports mixed-precision computation (INT4 to FP16), enabling powerful edge AI capabilities such as intelligent data processing, voice recognition, and image analysis for most terminal devices.

Application Scenarios

It is widely applicable across various industries and fields, including robotics, drones, cameras, edge computing, cloud gaming, computing services, intelligent security, and smart homes.

Robots
Robots
Drone
Drone
Smart Network Cameras
Smart Network Cameras
Edge Computing
Edge Computing
Computing Power Services
Computing Power Services
Smart Home
Smart Home
Interfaces
Specifications

AIO-8550JD4

Basic Specifications

SOC

Qualcomm QCS8550

CPU

Qualcomm Kryo® CPU: Octa-core 64-bit (1×GoldPlus@3.2GHz + (2+2)Gold@2.8GHz + 3×Silver@2.0GHz), 4nm advanced process, maximum frequency up to 3.36GHz

GPU

Adreno 740 GPU: Supports ray tracing technology, OpenGL ES 3.2, Vulkan 1.2, full-profile OpenCL 3.0, and Adreno NN Direct

ISP

Equipped with Qualcomm Spectra Cognitive ISP (Image Signal Processor), featuring three 18-bit 36MP ISPs

NPU

Dual eNPU V3: Equipped with 4 HVX (Hexagon Vector Extensions), 1 HMX (Hexagon Matrix Extension); Computing power up to 48 TOPS (INT8), 12 TOPS (FP16)

Codec

Video decoding: 8K@60fps/4K@240fps H.264/VP9/AV1 Video encoding: 8K@30fps/4K@120fps H.264 Supports concurrent 4K@60fps decoding and 4K@60fps encoding for wireless display scenarios

RAM

16GB LPDDR5x

Storage

256GB UFS4.0

Storage expansion

1 × M.2 M-KEY (supports expansion of SATA 3.0/PCIe NVMe SSD, compatible with 2242/2260/2280 form factors), 1 × TF Card

Power

DC 12V (5.5mm × 2.1mm，support 12V~24V wide voltage input)

Power consumption

Normal: 4.8W(12V/400mA), Max: 18W(12V/1500mA)

OS

Linux

Software support

Supports on-premises deployment of large-scale parameter models based on the Transformer architecture, such as large language models (LLMs) including the Deepseek-R1 series, Gemma series, Llama series, Qwen series, Phi series, etc. Supports the QNN AI inference framework, as well as various deep learning frameworks including TensorFlow, TensorFlow Lite, PyTorch, ONNX, etc.

Size

122.89mm × 85.04mm × 31.55mm

Weight

Without heatsink: 117g; With heatsink: 165g

Environment

Operating Temperature: -20℃～60℃, Storage Temperature: -20℃～70℃, Operating Humidity: 10%～90%RH (No condensation)

Interface Specifications

Network

Ethernet: 2 × RJ45(1000Mbps) WiFi: Extend WiFi/Bluetooth module through M.2 E-KEY (2230), supporting 2.4GHz/5GHz dual band WiFi6 (802.11a/b/g/n/ac/ ax) and Bluetooth 5.2 4G: Extend 4G LTE via Mini PCIe (Shared with 5G) 5G: Extend 5G via M.2 B-KEY (Shared with 4G, not mounted by default)

Video input

2 × MIPI D/C PHY (4 lanes DPHY or 3 trios CPHY) + 2 × MIPI D/C PHY (2 lanes DPHY or 1 trio CPHY)

Video output

1 × HDMI2.0 (4K@60Hz)

Audio

1 × 3.5mm Audio jack (Supports MIC recording, American standard CTIA)

USB

2 × USB3.0 (Max: 1A), 1 × Type-C (Flash)

DIP switch

1 × Auto Power-on DIP Switch (ON: Power on directly when connected; 1(OFF): Press Power button briefly to power on after connection) 1 × Type-C function selection(ON: Debug; 1(OFF): Flash)

Button

1 × Reset, 1 × Recovery, 1 × Power

Other interfaces

1 × FAN (4Pin-1.25mm), 1 × SIM Card 1 × Double-row pin headers (20Pin-2.0mm): USB2.0, UART, 2 × I2C, Line in, Line out, GPIO 1 × Phoenix connector (8Pin-3.5mm): 1 × RS485, 1 × RS232, 1 × CAN 2.0

Accessories

EC20 4G-LTE

$ 40.00

USB to UART Module

$ 5.90

WXT14R2201 WiFi Module

$ 15.00

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