Large MoE Performance: The Three Walls After Sparsity
Large MoE Performance: The Three Walls After Sparsity Sparsity made MoE cheap on paper. At production scale it made training harder than dense: total parameters grow with E, per-token FLOPs grow with k, and the gap between those two numbers is exactly where systems break. The useful framing is not “optimize the MoE kernel.” It is the one NVIDIA’s Megatron-Core MoE report uses (arXiv:2603.07685): Memory, Communication, and Compute Efficiency are three coupled walls. Push on one and pressure shows up in another. ByteDance’s MegaScale-MoE (arXiv:2505.11432) proves the same thesis from the other direction — on 1,440 Hoppers, communication was ~44% of forward time before their redesign, and fixing parallelism + overlap delivered 1.88× over Megatron-LM. ...