BM1688, the SOPHON AI processor, features an octa-core ARM Cortex-A53 with up to 1.6 GHz of frequency. Equipped with a neural network
acceleration engine TPU, it delivers peak performance of 32T@INT4 and 16T@INT8, 4T@FP16/BF16, and 0.5T@FP32. With support for
mainstream programming frameworks, this processor can be widely used in AI inference, computer vision, and more.
The AI box supports up to 16 channels of H.265 / H.264 1080P video decoding, 10 channels of H.265 / H.264 1080P video encoding,
and 16 channels of 1080P HD video processing (decoding + AI analysis). This meets the needs of various AI applications
such as face detection on video streaming, license plate recognition, and smart cities.
With dual 1000Mbps Ethernet, the AI box ensures high-speed and stable network communication,
meeting the needs of various application scenarios.
The AI box features an industrial-grade all-metal enclosure with an aluminum alloy structure for thermal conduction.
The side of the top cover features a grille design for external airflow and efficient heat dissipation,
ensuring computing performance and stability even under high-temperature operating conditions.
Its top cover is a porous hexagonal design, combining elegance with high efficiency. The compact,
exquisite device operates stably and meets the needs of various industrial-grade applications.
We offer SDKs, tutorials, technical documentation, and development tools to streamline and improve the development process.
AIBOX-1688 is widely used in intelligent surveillance, AI education, services based on computing power, edge computing,
private deployment of large models, and data security and privacy protection.
AIBOX-1688 | AIBOX-186 | ||
Basic Specificat |
SOC |
SOPHON BM1688 |
SOPHON CV186AH |
CPU |
Octa-core ARM Cortex-A53 @ 1.6GHz |
Hexa-core ARM Cortex-A53 @ 1.6GHz |
|
TPU |
Built-in SOPHGO neural network acceleration engine TPU, 32T@INT4 peak computing power, 16T@INT8 peak computing power, 4T@FP16/BF16computing power, 0.5T@FP32 computing power |
7.2T@INT8, 12T@INT4, and 1.5T@FP16/BF16 computing power |
|
Decoding/ Encoding |
Video decoding: H.264 / H.265 decoding (Max performance: 1920 * 1080@480FPS or 3840 * 2160 @120FPS) Video encoding: H.264 / H.265 encoding (Max performance: 1920 * 1080@300FPS or 3840 * 2160 @75 FPS) Image codec: JPEG/MJPEG Baseline codec (JPEG codec with a maximum resolution of 1080P@480 FPS) |
||
RAM |
8GB LPDDR4 (4GB/8GB/16GB optional) |
4GB LPDDR4 (4GB/8GB/16GB optional) |
|
Storage |
32GB eMMC (32GB/64GB/128GB/256GB optional) |
||
Storage Expansion |
1*M.2 (Expandable PCIe NVMe SSD(default support)/ SATA SSD(supported after software update), supports 2242/2260/2280) (inside the device),1*TF Card |
||
Power |
DC 12V/3A(DC 5.5*2.1mm) |
||
Power consumption |
Normal: 7.2W(12V/600mA), Max: 14.4W(12V/1200mA) |
Normal: 6W(12V/500mA), Max: 10.8W(12V/900mA) |
|
OS |
Linux |
||
Software Support |
・ The private deployment of ultra-large-scale parameter models under the Transformer architecture, including large language models such as Gemma-2B, LlaMa2-7B, ChatGLM3-6B, Qwen1.5-1.8B. ・ Traditional network architectures such as CNN, RNN, and LSTM; a variety of deep learning frameworks, including TensorFlow, PyTorch, MXNet, PaddlePaddle, and ONNX, as well as custom operator development ・ Docker container management technology |
・The private deployment of ultra-large-scale parameter models under the Transformer architecture, including large language models such as Gemma-2B, LlaMa2-7B, ChatGLM3-6B, Qwen1.5-1.8B. ・ A variety of deep learning frameworks, including TensorFlow, PyTorch, TensorRT, TFLite, PaddlePaddle, Caffe and ONNX, as well as custom operator development ・ Docker container management technology |
|
Size |
93.4mm × 93.4mm × 50 mm |
||
Weight |
≈ 500g |
||
Environment |
Operating Temperature: -20℃~60℃, Storage Temperature: -20℃~70℃, Storage Humidity: 10%~90%RH (non-condensing) |
||
Interface Specif |
Ethernet |
2*1000Mbps Ethernet |
|
Video output |
1*HDMI2.0(4K@60fps) |
||
USB |
2*USB3.0 (Current Limit: 1A) |
||
Other |
1*Type-C (USB 2.0 device mode only), 1*Console (Debug serial), 1*Power button, 1*Recovery |