EC-A3588JD4 AI Computer

Octa-core AI Processor RK3588

EC-A3588JD4 is equipped with the Rockchip flagship processor RK3588, an octa-core 64-bit CPU capable
of delivering 6 TOPS of computing power, enhancing performance for AI application scenarios.

Equipped with the aBMC management system

Supports the private deployment of expansive parameter models based on the Transformer framework,
including substantial language models like Gemma-2B, ChatGLM3-6B, Qwen1.5-1.8B, and Phi-3-3.8B.

Decoding of 8K
Ultra-High Definition Video

Enables 8K high-definition video decoding, delivering clearer
images and richer details, with a 2D hardware engine that
significantly enhances display performance.

32 GB of Massive Memory

Surpassing previous memory capacity limitations, it offers faster
response speeds and meets the demands of applications that
require substantial memory and large storage capacities.

Industrial-Grade Stability

Supports external watchdog for industrial-grade stability.
Compatible with multiple operating systems, suitable
for ARM PCs, edge computing, cloud servers,
intelligent NVRs, and other fields.

Comprehensive Expansion Interfaces

Specifications

EC-A3588JD4
Basic Specifications
SOC

Rockchip RK3588

CPU

Octa-core 64-bit processor (4×Cortex-A76+4×Cortex-A55) , main frequency up to 2.4GHz

GPU

ARM Mali-G610 MP4 quad-core GPU, support OpenGL ES3.2/OpenCL 2.2/Vulkan1.1, 450 GFLOPS

NPU

The computing power is up to 6TOPS(INT8), support INT4/INT8/INT16 mixed operations

ISP

Integrated 48MP ISP, support HDR and 3DNR

Codecs

Decoding: 8K@60fps H.265/VP9/AVS2, 8K@30fps H.264 AVC/MVC, 4K@60fps AV1, 1080P@60fps MPEG-2/-1/VC-1/VP8 Encoding: 8K@30fps H.265/H.264

RAM

LPDDR4/LPDDR4x (4GB/8GB/16GB optional, up to 32GB)

Storage

eMMC (32GB/64GB/128GB/256GB optional)

Storage Expansion

1 × TF Card, M.2 SATA3.0/PCIe NVMe SSD 2242/2260/2280 (Inside the computer)

OS

Android、Linux OS

Software support

・ Support the privatization deployment of ultra-large-scale parametric models under the Transformer architecture, such as   Gemma-2B, ChatGLM3-6B, Qwen-1.8B, Phi-3-3.8B and other large language models ・ It supports traditional network architectures such as CNN, RNN, and LSTM, and supports the import and export of RKNN   models; Support a variety of deep learning frameworks, including TensorFlow, TensorFlow Lite, PyTorch, Caffe, ONNX   and Darknet. It also supports the development of custom operators ・ Support Docker container management technology

Power

DC 12V (5.5mm × 2.1mm, support 9V~24V wide voltage input)

Size

188.0mm × 88.44mm × 50.65mm

Weight

Net weight of computer: 0.79kg, Total weight with package: 1.12kg

Environment

Operating Temperature: -20℃~60℃, Storage Temperature: -20℃~70℃, Storage Humidity: 10%~90%RH(non-condensing)

Interface
Internet

Ethernet: 2 × RJ45 (1000Mbps) WiFi: Extend WiFi/Bluetooth module through M.2 E-KEY (2230), support 2.4GHz/5GHz dual band WiFi6 (802.11a/b/g/n/ac/ax), Bluetooth5.2 4G: Extend 4G LTE via Mini PCIe

Video output

1 × HDMI2.1(8K@60fps or 4K@120fps)

Audio output

1 × 3.5mm Audio jack (Support MIC recording, American Standard CTIA)

USB

2 × USB3.0 (Max: 1A)

Other interfaces

1 × Type-C (OTG), 1 × SIM Card 1 × Phoenix connector (2×4PIN, 3.5mm pitch): 1 × RS485, 1 × RS232, 1 × CAN 2.0