华为：让DeepSeek的“专家们”动起来，推理延迟降10%！

Core Viewpoint - The article discusses Huawei's innovative approach to optimizing the performance of the Mixture of Experts (MoE) model through a technique called OmniPlacement, which addresses the load balancing issues between "hot" and "cold" experts, leading to significant improvements in inference latency and throughput. Group 1: MoE Model and Its Challenges - The MoE model allocates tasks to specialized expert networks, enhancing overall system performance [2] - Load balancing issues arise due to the uneven call frequency of expert networks, leading to performance limitations [3][5] - The disparity in call frequency can exceed an order of magnitude, causing delays in inference time and resource utilization [4][5] Group 2: Huawei's Solution - OmniPlacement - Huawei's OmniPlacement technique aims to optimize the deployment of experts to improve MoE model performance [8] - The approach involves three main steps: joint optimization based on computational balance, inter-layer redundant deployment of high-frequency experts, and near-real-time scheduling with dynamic monitoring [9][14][18] Group 3: Key Features of OmniPlacement - The OmniPlacement algorithm dynamically adjusts expert priorities and node allocations based on real-time statistics, reducing communication overhead [12] - The inter-layer redundant deployment strategy assigns additional instances to frequently called experts, alleviating their load and enhancing system throughput [15] - The near-real-time scheduling mechanism allows for dynamic resource allocation and predictive distribution based on historical data, improving system responsiveness [19][21] Group 4: Performance Improvements - The implementation of OmniPlacement in the DeepSeek-V3 system theoretically reduces inference latency by approximately 10% and increases throughput by about 10% [6][31] - The system demonstrates high adaptability across various MoE model scales and input data distributions, ensuring efficient resource utilization and stable operation [25][26] - The dynamic monitoring mechanism ensures rapid response to sudden load changes, maintaining system stability under high-demand scenarios [32] Group 5: Open Source Initiative - Huawei plans to open-source the OmniPlacement optimization method, promoting wider adoption and collaboration within the AI community [28]