Core-3588JD4 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.
upports 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.
Core-3588JD4 | ||
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) |
|
Power |
5V (voltage tolerance ± 5%) |
|
Power consumption |
Max: 13W(5V/2600mA), Normal: 2.8W(5V/560mA), Min: 0.175W(5V/35mA) |
|
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 |
|
Interface |
SODIMM (260 PIN, 0.5mm pitch) |
|
Size |
69.6mm × 45.0mm × 4.6mm |
|
Interface Specifications |
Weight |
≈18g |
Environment |
Operating Temperature: -20℃~60℃, Storage Temperature: -20℃~70℃, Storage Humidity: 10%~90%RH(non-condensing) |
|
Internet |
2 × Gigabit Ethernet (MDI interface is provided, and the core board has an onboard Ethernet PHY chip) Expandable WiFi6/Bluetooth 5.2 via SDIO3.0/PCIe3.0 Expandable 5G/4G LTE via USB3.1 (Gen1)/USB2.0 |
|
Video input |
MIPI CSI (2×4Lanes/4×2Lanes/1×4Lanes + 2×2Lanes) |
|
Video output |
1 × HDMI2.1 TX/eDP1.3 TX (8K@60Hz, HDMI supports HDCP2.3; Supports eDP1.3, 4K@60Hz, supports HDCP1.3; HDMI and eDP cannot work at the same time) |
|
Audio output |
2 × I2S (2 channels), 2 × SPDIF, 1 × PDM (8 channels, support multi-MIC array) |
|
USB |
2 × USB3.1(Gen1)OTG、1 × USB3.1(Gen1)HOST、2 × USB2.0 HOST、2 × USB2.0 OTG |
|
PCIe |
1 × PCIe3.0 (2×2lanes, 1×4lanes, 4×1lanes) 、3 × PCIe2.1 (1 lane) |
|
SATA |
3 × SATA3.0 (Multiplexed with PCIe 2.1) |
|
Watchdog |
Independent watchdog |
|
Other interfaces |
8 × I2C、7 × UART、4 × SPI、2 × ADC、15 × PWM、1 × SDMMC、2 × CAN、GPIO |