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Deploying massive Mixture-of-Experts (MoE) architectures like DeepSeek-V3/R1 requires a co-designed approach leveraging NVIDIA Blackwell’s fifth-generation Tensor Cores. This session details the transition to NVFP4 precision for MoE weights to significantly reduce memory load, coupled with FP4/FP8 KV caching to minimize attention layer footprint and enable higher concurrency. We will analyze the architectural shift to Expert Parallelism (EP) for expert layers to maximize FLOPS, and Attention Data Parallelism (ADP) for attention heads—avoiding redundant KV replication and converting Multi-Head Latent Attention (MLA) into Multi-Query Attention (MQA) via weight absorption. The talk will demonstrate advanced execution strategies, including DualPipe algorithms to overlap dispatch/combine communication with computation, and the integration of DeepGEMM and FlashInfer kernels. Finally, we will cover runtime optimizations using Programmatic Dependent Launch (PDL) and CUDA Graphs to minimize host latency, alongside Multi-Token Prediction (MTP) for accelerated speculative decoding.
