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CSR2-N72R3588S
CSB1-N10NOrinNX
EC-A3588JD4
EC-R3588SPC FD
AIBOX-OrinNano
EC-R3588RT 2G5
IPC-M10R800-A3399C V2
iCore-3576Q
iCore-3562JQ
Core-1688JD4
AIO-3576JD4
ROC-RK3576-PC
CT36L/CT36B
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.
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.
Enables 8K high-definition video decoding, delivering clearer
images and richer details, with a 2D hardware engine that
significantly enhances display performance.
Surpassing previous memory capacity limitations, it offers faster
response speeds and meets the demands of applications that
require substantial memory and large storage capacities.
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.
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 |
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