Moore Threads launches MTT AIBOOK laptop with self‑developed Yangtze River SoC

Dec 20, 2025 | Computers | Will Evans

Moore Threads launches MTT AIBOOK laptop with self‑developed Yangtze River SoCMoore Threads has officially introduced its new MTT AIBOOK laptop, powered by the company's first self‑developed Yangtze River intelligent SoC. The device is now available for pre‑order on JD.com at ¥9,999 (≈$1,380 USD) for the 32GB + 1TB configuration, with official sales beginning January 10, 2026.

The MTT AIBOOK features a 2.8K OLED display supporting a 120 Hz refresh rate, 10.7‑billion‑color depth, and 100% DCI‑P3 wide color gamut. The chassis is built from 6‑series aluminum‑magnesium alloy, finished with ultra‑fine sandblasting and anodizing. The laptop measures 14.9 mm thick (≈0.59 in) and weighs 1.28 kg (≈2.82 lb).

At its core is Moore Threads' self‑developed AIBOOK AI chip, integrating a high‑performance all‑large‑core CPU and a full‑feature Moore Threads GPU. It supports the MUSA unified architecture and delivers up to 50 TOPS of heterogeneous AI compute. The system includes 32 GB of LPDDR5X 7500 MT/s unified memory, enabling strong performance across AI workloads, model debugging, graphics rendering, scientific computing, and video processing.

Cooling is handled by a 7,752 mm² (≈12 in²) ultra‑thin vapor‑chamber heat spreader paired with dual high‑performance turbine fans. Fan speed can be intelligently adjusted between 2,100 and 6,500 rpm depending on workload.

The laptop includes a short‑travel 1.2 mm keyboard and a large 135 mm x 80 mm (≈5.3 in x 3.1 in) multi‑touch trackpad. Audio is delivered through four array microphones and four ultra‑linear speakers. The system supports seamless switching between Windows, Linux, and Android.

On the software side, the MTT AIBOOK includes an AI development environment, AI digital‑human cloning, and an AI office assistant. Applications such as Moyin Notes support personal‑knowledge‑base creation, AI‑assisted document processing, and automatic PPT generation. The laptop can also collaborate with cloud servers to handle more complex AI workloads.

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