Wan 2.2 video generation in less than 60 seconds

This post details the development of a highly optimized inference runtime for the Wan 2.2 video generation model, achieving 3x faster inference on NVIDIA Blackwell GPUs and 2.5x faster on NVIDIA Hopper GPUs compared to the default runtime. Key technical contributions include: custom CUDA kernel optimizations for GEMM, RoPE attention, LayerNorm, and RMSNorm tailored to Wan 2.2's input shapes; bypassing serial all-to-all communication in Ulysses Sequence Parallelism (USP) using NCCL and Blackwell's asynchronous programming; and setting `offload_model` to false to maximize GPU throughput. The post also discusses benchmarking methodology, inference parameters, and ongoing research into lossy optimizations like attention caching and FP4 quantization.