单卡1000 TFLOPS，摩尔线程旗舰级计算卡首曝，性能逼近Blackwell

Core Insights - The release of GLM-5 by Zhipu AI has sparked significant industry discussion, highlighting its coding capabilities as the top in global open-source models and fourth overall [1] - The MTT S5000 from Moore Threads has achieved Day-0 compatibility with GLM-5, showcasing impressive hardware specifications that rival NVIDIA's H100 [1][6] Group 1: Performance and Specifications - The MTT S5000 boasts a single-card performance of 1000 TFLOPS, with 80GB of memory and a memory bandwidth of 1.6TB/s, matching NVIDIA's H100 in key specifications [6][7] - The introduction of hardware-level FP8 Tensor Core in MTT S5000 has significantly enhanced its performance, reportedly surpassing H100 in precision [7] - In practical tests, MTT S5000 demonstrated performance approximately 2.5 times that of its competitor H20 in typical end-to-end inference and training tasks [9] Group 2: Ecosystem and Software Integration - The success of Day-0 compatibility is attributed to Moore Threads' agile MUSA software stack, which has over 80% coverage for native operator unit tests, reducing porting costs significantly [3] - The MUSA software platform allows seamless integration with major frameworks like PyTorch and Megatron-LM, enabling zero-cost code migration for developers [11] Group 3: Scalability and Efficiency - The "Kua'e" cluster built on MTT S5000 has achieved a floating-point operation capability of 10 Exa-Flops, marking a significant advancement in large-scale computing [9] - The system maintains over 90% linear scaling efficiency from 64 to 1024 cards, indicating nearly synchronized training speed increases with added computational power [10] Group 4: Real-World Applications - In training scenarios, the S5000 has shown a training loss difference of only 0.62% compared to NVIDIA's H100, demonstrating its accuracy and stability in replicating top-tier model training processes [11] - For inference, the S5000 achieved a prefill throughput of over 4000 tokens/s and a decode throughput exceeding 1000 tokens/s, significantly reducing memory usage and ensuring low response latency in high-concurrency environments [12]