MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints

Mixture of Experts (MoE) LLMs, with sparse activation patterns, offer a promising approach to scaling language models while avoiding proportional cost increases. However, their large parameter sizes pose deployment challenges in resource-constrained environments with limited GPU memory. Typical deployments use CPU-GPU hybrid execution, where the GPU handles compute-intensive GEMM operations and the CPU processes the attention mechanism. This setup introduces a challenge: optimizing resource utilization across CPU and GPU. Prior work designs system optimizations based on performance models with a limited scope that don’t capture complex hardware-system interactions. Therefore, they neither identify nor achieve hardware limits.