Ultra-high energy efficiency ratio! Supports private deployment of mainstream large models.
Superior energy efficiency! Support the private deployment of
mainstream large models. Bring private AI capability to meet
individual AI deployment needs.
The private deployment of large models
The box is equipped with an ARM Mali G52 MC3 GPU, delivering up to 6 TOPS of computing power. This enables advanced intelligent data
processing, speech recognition, and image analysis, effectively fulfilling the AI application demands for edge computing
on a wide range of terminal devices.
RK3576, the new octa-core 64-bit high-performance AIOT processor, features a big.LITTLE architecture (4×A72 +4×A53), an advanced
lithography process, and a frequency of up to 2.2 GHz. It ensures powerful support for high-performance computing and multitasking.
The Mali-G52 MC3 GPU, delivering 145G FLOPS, is capable of supporting efficient heterogeneous computing
to meet the demands of graphics-intensive applications.
This device supports 8K@30fps / 4K@120fps decoding (H.265 / HEVC, VP9, AVS2, and AV1),
4K@60fps decoding (H.264 / AVC), and 4K@60fps encoding (H.265 / HEVC, H.264 / AVC).
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.
Support Linux OS. This provides a safe and stable system environment for product research and production. We offer SDKs, tutorials, technical
documentation, and development tools to streamline and improve the development process.
n deployment of large models, data security, and privacy protection.
AIBOX-3576 | AIBOX-3588 | AIBOX-3588S | |||
Basic Specificat |
SOC |
Rockchip RK3576 |
Rockchip RK3588 |
Rockchip RK3588S |
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CPU |
Octa-core 64-bit processor(4×A72+4×A53), main frequency up to 2.2GHz |
Octa-core 64-bit processor(4×Cortex-A76+4×Cortex-A55), main frequency up to 2.4 GHz |
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GPU |
G52 MC3@1GHz, supports OpenGL ES 1.1/2.0/3.2, OpenCL 2.0, Vulkan 1.1, embedded high-performance 2D acceleration hardware |
ARM Mali-G610 MP4 quad-core GPU, supports OpenGL ES3.2/ OpenCL 2.2/Vulkan1.1, 450 GFLOPS |
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NPU |
6 TOPS NPU, supports INT4/8/16/FP16/ BF16/TF32 mixed operations |
6 TOPS NPU, supports INT4/INT8/INT16 mixed operations |
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ISP |
Built-in 16 million pixel ISP, support low-light noise reduction, support RGB-IR sensor, support up to 120dB HDR, AI-ISP to improve low-noise image effect |
Integrated 48MP ISP with HDR&3DNR |
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Encoding Decoding |
Decoding: 8K@30fps/4K@120fps: H.265/HEVC, VP9, AVS2, AV1, 4K@60fps: H.264/AVC Encoding: 4K@60fps: H.265/HEVC、H.264/AVC |
Decoding: 8K@60fps/4K@120fps 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 |
Decode: 8K@60fps H.265/VP9/AVS2 8K@30fps H.264 AVC/MVC 4K@60fps AV1 1080P@60fps MPEG-2/-1/VC-1/VP8 Encode: 8K@30fps H.265/H.264 |
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RAM |
LPDDR4 (4GB/8GB/16GB optional) |
LPDDR4 (4GB/8GB/16GB/32GB optional) |
LPDDR5 (4GB/8GB/16GB/32GB optional) |
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Storage |
eMMC (16GB/32GB/64GB/128GB/256GB optional), UFS2.0 (Only AIBOX-3576 optional) |
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Storage Expansion |
1 × M.2 (Expandable SATA 3.0/PCIe NVMe SSD, supports 2242/2260/2280; Inside the computer), 1 × TF Card |
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Power |
DC 12V/2A(DC 5.5 × 2.1mm) |
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Power consumption |
Normal: 1.2W(12V/100mA) Max: 7.2W(12V/600mA) Min: 0.72W (12V/6mA) |
Normal: 2.64W(12V/220mA) Max: 14.4W(12V/1200mA) Min(Sleep): 0.18W(12V/15mA) |
Normal: 1.26W(12V/105mA) Max: 13.2W(12V/1100mA) Min(Sleep): 0.18W(12V/15mA) |
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OS |
Linux |
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Software support |
·Support the privatization deployment of ultra-large-scale parametric models under the Transformer architecture, such as Gemma series, ChatGLM series, Qwen series, Phi series 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 |
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Size |
93.4mm × 93.4mm × 50mm |
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Weight |
≈ 500g |
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Environment |
Operating: -20℃~60℃, Storage: -20℃~70℃, Storage Humidity: 10%~90%RH (non-condensing) |
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Interface Specif |
Ethernet |
2 × Gigabit Ethernet (1000Mbps/RJ45) |
1 × Gigabit Ethernet (1000Mbps/RJ45) |
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Video Output |
1 × HDMI2.1(4K@120fps) |
1 × HDMI2.1(8K@60fps) |
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USB |
2 × USB3.0 (Max: 1A), 1 × Type-C (Firmware flashing) |
2 × USB3.0 (Max: 1A), 1 × Type-C (Can be used as a firmware flashing port. Set to USB2.0 HOST after booting up) |
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Button |
1 × Power, 1 × MaskRom |
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Other interfaces |
1 × Console (Debug serial) |