Guide Covers MoE Routing, ZeRO-Offload, and Inference Optimization for Large LLMs

A five-part developer series on GPU optimization for large language models concludes with techniques for scaling beyond dense Transformer architectures. The final installment covers Mixture of Experts (MoE), a method used by models like Grok, Mixtral, and Gemini to reach trillion-parameter scales without proportionally increasing compute costs by activating only a small subset of expert networks per token. The guide demonstrates implementing a CUDA top-k gating router kernel and expert parallelism using all-to-all communication via NCCL and RCCL libraries across multi-GPU setups. It also addresses the VRAM bottleneck by explaining ZeRO-Offload, which shifts optimizer states and gradients to system RAM while overlapping GPU computation with PCIe data transfers. Additional topics include CUDA and HIP graph optimization to reduce kernel launch overhead, along with building an inference server using KV caching and PagedAttention.