华为发布OmniPlacement技术，实现超大规模MoE专家最优动态部署，提升昇腾推理系统吞吐10%

Core Viewpoint - The article discusses the challenges and advancements in the Mixed Expert Model (MoE) technology, particularly focusing on the load balancing issues and the introduction of the OmniPlacement strategy by Huawei to enhance inference performance [2][4][12]. Group 1: Challenges in MoE Models - The MoE models face significant challenges, particularly the "cold and hot expert" phenomenon, where some experts are frequently called (hot experts) while others are rarely used (cold experts), leading to uneven load distribution [2][4]. - This imbalance results in increased inference latency and limited throughput, as underutilized resources restrict overall system performance [3][14]. Group 2: OmniPlacement Strategy - Huawei's OmniPlacement strategy addresses these challenges through expert reallocation, inter-layer redundancy deployment, and near-real-time dynamic scheduling, significantly improving MoE model inference performance [4][12]. - The strategy includes a joint optimization algorithm that reduces load imbalance by analyzing expert activation data and optimizing deployment order based on call frequency and computational needs [5][14]. Group 3: Key Features of OmniPlacement - OmniPlacement employs inter-layer redundancy deployment to alleviate the pressure on hot experts by allocating additional redundant instances, thus enhancing system throughput [5][12]. - The framework supports dynamic resource allocation based on real-time resource usage and expert call frequency, allowing for predictive resource distribution to minimize performance discrepancies between hot and cold experts [6][9]. Group 4: Testing and Results - Comprehensive testing on the DeepSeek-V3 model demonstrated that OmniPlacement reduces average inference latency by approximately 10% compared to baseline methods, primarily due to dynamic expert allocation and communication domain optimization [12][14]. - The system's throughput improved by about 10%, reflecting a significant increase in resource utilization, especially in high-concurrency scenarios [14]. Group 5: Future Directions - Future research will focus on developing smarter scheduling algorithms and adaptive expert selection mechanisms to further enhance the system's adaptability to complex inputs [15][16]. - The OmniPlacement framework aims to expand its functionality to support more types of MoE models, increasing its versatility and applicability in various industrial settings [16].