Trillion-Parameter MoE Model Inference: Optimization Guide for Sparse Attention and Long Contexts

As large language models scale to trillions of parameters with native multimodal capabilities and million-token context windows, underlying compute architectures and inference frameworks face unprecedented challenges. This article explores general optimization practices for deploying ultra-large Mixture-of-Experts (MoE) models on heterogeneous computing clusters. It highlights key technical areas including operator collaboration for hybrid attention, distributed KV cache management, and cross-device communication optimization to ensure low latency and high throughput for long-horizon tasks on Day 0.