The Chinese company is facing significant challenges due to its inclusion on the U.S. Department of Commerce’s Entity List, restricting access to advanced manufacturing processes. Despite these obstacles, the firm has secured partnerships with major Chinese state-run telecom operators and technology companies, focusing on developing new computing cluster projects. A recent financing round raised approximately $343.7 million will help Moore Threads’ ambitious expansion plans. However, limited access to cutting-edge semiconductor fabrication technologies may constrain the company’s future growth. Nonetheless, creating a scale-out server infrastructure with up to 10,000 GPUs is vital for LLM training and inference, especially as Chinese AI labs catch up to Western labs in terms of the performance of their AI models.
Moore Threads MTLink Scales Up to 10,000 Home-Grown GPUs in AI Cluster
Chinese GPU manufacturer Moore Threads has announced a significant upgrade to its KUAE data center server. The company now has the ability to connect up to 10,000 GPUs in a single cluster, marking a huge leap in its scale-out capabilities for artificial intelligence and high-performance computing applications. The enhanced KUAE server incorporates eight MTT S4000 GPUs, leveraging Moore Threads’ proprietary MTLink interconnect technology. These GPUs, based on the MUSA architecture, each feature 128 tensor cores and 48 GB of GDDR6 memory, delivering a bandwidth of 768 GB/s. While the full performance metrics of a 10,000-GPU cluster remain undisclosed, the sheer scale of 1,280,000 tensor cores suggests decent computing potential. Moore Threads’ GPUs currently lag behind NVIDIA’s GPU offerings in terms of performance. However, the company claims its MTT S4000 remains competitive against certain NVIDIA models, particularly in large language model training and inference tasks.