MoE推理「王炸」组合：昇腾×盘古让推理性能狂飙6-8倍

Core Viewpoint - The article emphasizes the significant advancements in the Pangu Pro MoE 72B model developed by Huawei, highlighting its efficiency in large model inference through innovative techniques and optimizations, which have led to substantial performance improvements in AI applications [2][23]. Group 1: Model Performance and Optimization - The Pangu Pro MoE model achieves a 6-8 times improvement in inference performance through system-level optimizations, including high-performance operator fusion and model-native speculative algorithms [3][23]. - The model's throughput reaches 321 tokens/s on the Ascend 300I Duo and can soar to 1528 tokens/s on the Ascend 800I A2, showcasing its capability to fully leverage hardware potential [3][24]. Group 2: Hierarchical and Hybrid Parallelism - Huawei introduces a novel Hierarchical & Hybrid Parallelism (H P) strategy, which enhances efficiency by allowing specialized communication and computation without the need for all components to engage simultaneously [6][7]. - This strategy results in a 33.1% increase in decode throughput compared to traditional parallel processing methods [7]. Group 3: Communication Optimization - The TopoComm optimization scheme reduces static overhead and improves data transmission efficiency, achieving a 35% reduction in synchronization operations and a 21% increase in effective bandwidth [9][12]. - The introduction of mixed quantization communication strategies leads to a 25% reduction in communication data size and a 39% decrease in AllGather communication time [9]. Group 4: Operator Fusion and Efficiency - The development of fusion operators like MulAttention and SwiftGMM addresses the inefficiencies of traditional operators, significantly enhancing memory access and computation scheduling [15][18]. - MulAttention achieves a 4.5 times acceleration in attention computation, while SwiftGMM reduces inference latency by 48.7% [16][18]. Group 5: Dynamic Pruning and Collaborative Optimization - The PreMoE dynamic pruning algorithm enhances inference throughput by over 10% by selectively activating relevant experts for specific tasks [21]. - The TrimR and SpecReason algorithms optimize the reasoning process, reducing unnecessary computation and improving throughput by 30% [20][22]. Group 6: Overall System Optimization - The comprehensive optimization of the Ascend Pangu inference system establishes a robust foundation for high-performance, large-scale, and cost-effective AI model deployment [28].