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EC-A8550JD4 AI Computer

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  

· Full Aluminum Alloy Shell for Heat Dissipation  

· Rich Expansion Interfaces

· Comprehensive Development Toolchain

Manual Wiki Download

Gemma

Llama

Qwen

Phi

EC-A8550JD4
EC-A8550JD4 AI Computer

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. It includes multiple expansion interfaces such as HDMI 2.0, RS485, RS232, and USB 3.0, and provides AI model optimization tools, wiki tutorials, and other technical resources for efficient secondary 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

It supports the QNN AI inference framework and is compatible with deep learning frameworks such as 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.

Full Aluminum Alloy Shell

Featuring an industrial-grade all-aluminum alloy casing with fanless cooling design, it ensures stable 24/7 operation to meet diverse industrial application requirements.

Rich Expansion Interfaces

It features expansion interfaces such as a Gigabit Ethernet port, HDMI 2.0, RS485, RS232, CAN, USB 3.0, Type-C, TF Card slot, and SIM Card slot, meeting the peripheral expansion requirements of various scenarios.

Development Toolchain

It provides AI model optimization tools, accompanying source code, tutorials, and technical documentation, lowering the development barrier, simplifying the development process, and making development more efficient.
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 used in industries such as robotics, drones, smart cameras, edge computing, intelligent security, and smart home.

Robots
Robots
Drone
Drone
Smart Network Cameras
Smart Network Cameras
Edge Computing
Edge Computing
Intelligent Security
Intelligent Security
Smart Home
Smart Home
Interfaces
Specifications

EC-A8550JD4

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

NPU

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

ISP

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 PCIe NVMe SSD, compatible with 2242/2260/2280 form factors; Located at the bottom of the computer), 1 × TF Card

Power

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

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

188.0mm × 88.44mm × 50.65mm

Weight

Net weight of computer: 0.79kg, Total weight with package: 1.15kg

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 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)

Others

1 × SIM Card, 1 × Phoenix connector (8Pin-3.5mm): 1 × RS485, 1 × RS232, 1 × CAN 2.0
Accessories

RM500Q-GL 5G

$ 399.00

EC20 4G-LTE

$ 39.00

WXT14R2201 WiFi Module

$ 15.00

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