
EC-ThorT5000

CSB1-N4AGXOrin

CSR2-N72R3399

EC-ThorT5000

CT36L/CT36B

EC-ThorT5000
· High-Performance AI Processor QCS8550
· Deployment of Large AI Models
· Supports Ray Tracing Technology
· 8K Video Encoding and Decoding
· Qualcomm Cognitive ISP with 36MP Triple Camera
· 4nm Low-Power Design for Hardware Iteration
· Compact Form Factor with Gold-Finger Package
· Comprehensive Development Toolchain
Qwen
Llama
DeepSeek
Gemma
Powered by the Qualcomm QCS8550 AI processor with a 48 TOPS NPU, it supports mainstream AI models and features the Adreno 740 GPU for ray tracing and 8K video. The cognitive ISP supports up to a 100MP single or triple 36MP camera, all in a compact design with a 260-pin gold finger interface.
AI Processor QCS8550
Private Deployment
Ray Tracing Technology
8K Video Codec
36MP Triple Camera
4nm Low-Power Design
Gold-Finger Package
Development Toolchain
Integrated 48 TOPS NPU supports mixed-precision computation (INT4 to FP16), enabling powerful edge AI capabilities such as intelligent data processing, voice recognition, and image analysis for most terminal devices.
Widely used in industries such as robotics, smart cameras, drones, edge computing, intelligent security, and smart home.

CORE-8550JD4 |
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Basic Specifications |
SOC |
Qualcomm QCS8550 |
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CPU |
Qualcomm Kryo® CPU: Octa-core 64-bit (1×GoldPlus@3.2GHz + (2+2)Gold@2.8GHz + 3×Silver@2.0GHz), 4nm advanced process, maximum frequency up to 3.36GHz |
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GPU |
Adreno 740 GPU: Supports ray tracing technology, OpenGL ES 3.2, Vulkan 1.2, full-profile OpenCL 3.0, and Adreno NN Direct |
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ISP |
Equipped with Qualcomm Spectra Cognitive ISP (Image Signal Processor), featuring three 18-bit 36MP ISPs |
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NPU |
Dual eNPU V3: Equipped with 4 HVX (Hexagon Vector Extensions), 1 HMX (Hexagon Matrix Extension); Computing power up to 48 TOPS (INT8), 12 TOPS (FP16) |
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Codec |
Video decoding: 8K@60fps/4K@240fps H.265/H.264/VP9/AV1 Video encoding: 8K@30fps/4K@120fps H.265/H.264 Supports concurrent 4K@60fps decoding and 4K@60fps encoding for wireless display scenarios |
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RAM |
16GB LPDDR5x |
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Storage |
256GB UFS4.0 |
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Power |
5V (voltage tolerance ±5%) |
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Power consumption |
Normal: 3W(5V/600mA), Max: 10W(5V/2000mA), Min(Sleep): 0.4W(5V/80mA) |
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OS |
Ububtu |
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Software support |
Supports on-premises deployment of large-scale parameter models based on the Transformer architecture, such as large language models (LLMs) including the Deepseek-R1 series, Gemma series, Llama series, Qwen series, Phi series, etc. Supports the QNN AI inference framework, as well as various deep learning frameworks including TensorFlow, TensorFlow Lite, PyTorch, ONNX, etc. |
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Interface type |
SODIMM (260PIN, 0.5mm pitch) |
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Size |
69.6mm × 45.0mm × 4.3mm |
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Weight |
≈16g |
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Environment |
Operating Temperature: -20℃~60℃, Storage Temperature: -20℃~70℃, Operating Humidity: 10%~90%RH (No condensation) |
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Interface Specifications |
Network |
Supports 2×Gigabit Ethernet (via PCIe expansion; onboard Ethernet PHY chip on core board); 5G/4G expandable |
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Video input |
2 × MIPI D/C PHY (4 lanes DPHY or 3 trios CPHY) + 2 × MIPI D/C PHY (2 lanes DPHY or 1 trio CPHY) Supports 36MP triple camera / 64MP + 36MP dual camera / 108MP single camera |
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Video output |
1 × HDMI2.0 (4K@60Hz), 1 × DP1.4 (via Type-C output) |
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Audio |
2 × I2S (2 data channels per channel; supports 4-channel TX/RX, TDM/PCM modes, 48kHz sampling rate) |
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PCIe |
1 × PCIe3.0 (2lanes), 1 × PCIe4.0 (2lanes) |
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USB |
1 × USB3.1 Gen2 DRD with DP, 2 × USB3.0 HOST (via PCIe expansion) |
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Others |
3 × CCI I2C, 2 × I3C/I2C, 2 × SPI, 4 × UART, 1 × ADC, 1 × SDMMC, GPIOs |
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