HomeMoortech S5000 GPU Breakthrough Powers China Mobile's Jiutian AI Model
At the upcoming 9th Digital China Summit, China Mobile's self-developed "Jiutian" 35B general-purpose large language model will make its official public debut. As a significant advancement for the domestic computing ecosystem, Moore Threads recently announced that its flagship, full-featured GPU, the MTT S5000, has completed full-process adaptation and inference verification for this model.
The core of this adaptation lies in deep integration. Leveraging its proprietary MUSA software stack and the SGLang-MUSA high-performance inference engine, Moore Threads successfully implemented the entire inference pipeline for the "Jiutian" 35B model. Through collaborative optimization of the MUSA C development framework, the muDNN computing library, and the open-source MATE operator library, the MTT S5000 has been finely tuned for the specific attention mechanisms and long-sequence inference requirements of large models. This ensures efficient and stable performance when processing lengthy texts and handling high-concurrency requests.
The MTT S5000 computing card, serving as the technical foundation for this adaptation, has demonstrated exceptional capabilities. Built on the fourth-generation MUSA "Pinghu" architecture, this GPU delivers a maximum AI dense computing power of up to 1000 TFLOPS per card. Its hardware configuration features 80GB of high-capacity VRAM with a memory bandwidth of 1.6 TB/s, supporting full-precision computing from FP8 to FP64. Furthermore, a high inter-card interconnect bandwidth of 784 GB/s ensures excellent scalability in complex intelligent computing scenarios.
This collaboration not only validates the reliability of domestic GPUs in supporting core large models from central state-owned enterprises but also highlights Moore Threads' maturity in high-performance operator optimization and software ecosystem development. With the official launch of the "Jiutian" 35B model, this "domestic large model + domestic computing power" combination provides a highly relevant practical case for achieving independent and controllable computing infrastructure.
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At the upcoming 9th Digital China Summit, China Mobile's self-developed "Jiutian" 35B general-purpose large language model will make its official public debut. As a significant advancement for the domestic computing ecosystem, Moore Threads recently announced that its flagship, full-featured GPU, the MTT S5000, has completed full-process adaptation and inference verification for this model.
The core of this adaptation lies in deep integration. Leveraging its proprietary MUSA software stack and the SGLang-MUSA high-performance inference engine, Moore Threads successfully implemented the entire inference pipeline for the "Jiutian" 35B model. Through collaborative optimization of the MUSA C development framework, the muDNN computing library, and the open-source MATE operator library, the MTT S5000 has been finely tuned for the specific attention mechanisms and long-sequence inference requirements of large models. This ensures efficient and stable performance when processing lengthy texts and handling high-concurrency requests.
The MTT S5000 computing card, serving as the technical foundation for this adaptation, has demonstrated exceptional capabilities. Built on the fourth-generation MUSA "Pinghu" architecture, this GPU delivers a maximum AI dense computing power of up to 1000 TFLOPS per card. Its hardware configuration features 80GB of high-capacity VRAM with a memory bandwidth of 1.6 TB/s, supporting full-precision computing from FP8 to FP64. Furthermore, a high inter-card interconnect bandwidth of 784 GB/s ensures excellent scalability in complex intelligent computing scenarios.
This collaboration not only validates the reliability of domestic GPUs in supporting core large models from central state-owned enterprises but also highlights Moore Threads' maturity in high-performance operator optimization and software ecosystem development. With the official launch of the "Jiutian" 35B model, this "domestic large model + domestic computing power" combination provides a highly relevant practical case for achieving independent and controllable computing infrastructure.
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