AIBOX-8550 QCS8550 Embedded Mini PC

AIBOX-8550 QCS8550 Embedded Mini PC

$1,109.00

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AIBOX-8550 Computers

Features the Qualcomm QCS8550 octa-core AI processor with an integrated 48 TOPS NPU, supporting mainstream AI models and deep learning frameworks. The built-in Adreno 740 GPU enables ray tracing and 8K video codec. Housed in an industrial-grade all-metal casing for efficient heat dissipation, it ensures 24/7 stable operation to meet rigorous industrial demands.

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) and mainstream deep learning frameworks like TensorFlow, PyTorch, Caffe and ONNX.

Ray Tracing Technology

Equipped with the Adreno 740 GPU, it delivers full ray tracing support and is compatible with OpenGL ES 3.2, Vulkan 1.2, and OpenCL 3.0. Combined with Adreno Neural Processing, it significantly boosts both graphics and AI computing performance.

8K Video Codec Support

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.

Network Capabilities

Supports dual Gigabit Ethernet for high-speed, stable communication and runs on the secure Linux OS, ensuring reliability across diverse application scenarios.

All-Metal Enclosure

Housed in an industrial-grade all-metal enclosure with an aluminum alloy structure for effective heat conduction, it features a side grille for airflow and a perforated hexagonal top cover for efficient cooling and a sleek aesthetic. This compact yet robust design ensures stable performance in high-temperature environments, meeting various industrial application needs.

Rich Expansion Interfaces

Offers a comprehensive set of expansion ports, including dual Gigabit Ethernet, dual USB 3.0, TF Card, Type-C, HDMI 2.0, and Console, providing flexible connectivity for a wide range of peripherals.

Development Toolchain

We provide a full suite of development resources—including an AI model optimization tool, video processing SDK, source code, tutorials, and technical documentation—to lower the learning curve, streamline workflows, and accelerate development.

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

Widely applicable in industries such as intelligent surveillance, AI education, computing power services, edge computing, private deployment of large models, data security, and privacy protection.

Intelligent Security

AI Education

Computing Power Services

Edge Computing

Private Deployment of Large Models

Data Security

Interfaces

Specifications

AIBOX-8550
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.265/H.264/VP9/AV1 Video encoding: 8K@30fps/4K@120fps H.265/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 (Expandable PCIe NVMe SSD, supports 2242/2260/2280 specifications; Inside the device), 1 × TF Card
	OS	Ububtu
	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/5A (5.5 × 2.1mm)
	Power consumption	Normal: 4.32W(12V/360mA), Max: 20W(12V/1670mA), Min(Sleep): 0.6W(12V/50mA)
	Dimension	93.4mm × 93.4mm × 50.0mm
	Weight	≈ 500g
	Environment	Operating Temperature: -20℃～60℃, Storage Temperature: -20℃～70℃, Operating Humidity: 10%～90%RH (No condensation)
Interface Specifications	Ethernet	2 × Gigabit Ethernet (1000Mbps/RJ45)
	Console	1 × Console (Debug serial)
	Video output	1 × HDMI2.0 (1080P)
	USB	2 × USB3.0 (Max: 1A), 1 × Type-C (For download)
	Button	1 × Power, 1 × Recovery