Ultra-high energy efficiency ratio! Supports private deployment of mainstream large models.
Support the private deployment of mainstream large models.
Bring private AI capability to meet individual AI deployment needs.
The box is 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.
RK3588, The new-generation octa-core 64-bit high-performance AIOT processor RK3588 adopts an 8nm LP process with a maximum
clock speed of 2.4GHz. It integrates an ARM Mali-G610 MP4 quad-core GPU and is equipped with a built-in AI accelerator NPU,
providing computing power of 6 TOPS. The powerful RK3588 can deliver optimized performance for various AI application scenarios.
It supports 8K@60fps H.265/VP9 video decoding and 8K@30fps H.265/H.264 video encoding, with simultaneous encoding and decoding
capabilities. It can achieve a maximum of 32 channels of 1080P@30fps decoding and 16 channels of 1080P@30fps encoding.
With dual 1000Mbps Ethernet, the AI box ensures high-speed and stable network communication,
meeting the needs of various application scenarios.
Equipped with a full metal shell and aluminum alloy structure for thermal conductivity, the top cover shell side adopts a banner grille
design to ensure external air circulation, efficient heat dissipation, and ensure computing performance and
stability under high temperature operation
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.
AIBOX-3588 is widely used in intelligent surveillance, AI education, services based on computing power, edge computing,
private 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) |