AIO-8550JD4 QCS8550 AI Motherboard

AIO-8550JD4 QCS8550 AI Motherboard

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AIO-8550JD4 AI Motherboard

Equipped with the Qualcomm QCS8550 hexa-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.

Al Processor QCS8550

Features an hexa-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.

Support 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

Drone

Smart Network Cameras

Edge Computing

Computing Power Services

Smart Home

Interfaces

Specifications

AIO-8550JD4
Basic Specifications	SOC	Qualcomm QCS8550
	CPU	Qualcomm Kryo® CPU: Hexa-core 64-bit (1×GoldPlus@3.2GHz + 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 48TOPS (INT8), 12TOPS (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
	OS	Ubuntu, Android, Yocto Linux Software Stack
	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.
	Power	DC 12V (5.5mm × 2.1mm，support 12V~24V wide voltage input)
	Power consumption	Normal: 9.6W(12V/800mA), Max: 21.6W(12V/1800mA), Min(Sleep): 0.78W(12V/65mA)
	Dimension	122.89mm × 85.04mm × 31.55mm
	Weight	Without heatsink: 124g; With heatsink: 171g
	Environment	Operating Temperature: -20℃～60℃, Storage Temperature: -20℃～70℃, Operating Humidity: 10%～90%RH (No condensation)
Interface Specifications	Internet	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 (Default: 1080P@60Hz, Max: 4K@60Hz)
	Audio output	1 × 3.5mm Audio jack (Supports MIC recording, American standard CTIA)
	USB	2 × USB3.0 (Max: 1A), 1 × Type-C (Flash)
	Button	1 × Reset, 1 × Recovery, 1 × Power
	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)
	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