October 2025 monthly summary for flashinfer-ai/flashinfer: Key feature delivered is FP8 Block Scaling MoE support for SM90 (Hopper) using fused Cutlass operations. This work introduces FP8 kernel definitions and implementations, leveraging Tensor Memory Access (TMA) and Warp Group Matrix Multiply Accumulate (WGMMA) for optimized FP8 performance. It includes kernel logic for FP8 data handling, shared memory management, and integration with the FP8 Block Scaling MoE pathway. The change is tracked in commit 8276d03c368e49b25736a97d29d6d70e089be985 (feat:enable fp8 blockscale moe for fused cultass for sm90 (#1819)).

September 2025 — Repository: bytedance-iaas/vllm. Focused on delivering high-performance MXFP4 fused CUTLASS MoE kernel with testing and FlashInfer backend integration. Key outcomes include enabling MXFP4 fused MoE kernel on Blackwell (SM 10.0) and Hopper (SM 9.0) GPUs, introducing comprehensive tests, and integrating FlashInfer's CUTLASS backend to accelerate MoE workloads. No major bugs reported in scope for this period. Business impact: higher inference throughput for Mixture-of-Experts on next-gen GPUs, improved reliability via end-to-end tests, and smoother production readiness with FlashInfer integration. Technologies demonstrated: CUDA kernel development, CUTLASS, MXFP4 quantization, FlashInfer backend integration, MoE architectures, GPU performance testing, and test automation.

In August 2025, I delivered focused MoE performance and robustness improvements for flashinfer, emphasizing cross-architecture optimization and safer MoE execution. Key work includes mixed-precision MoE kernel support across SM100 and SM90, with SwigluBias activation, plus robustness enhancements including OOB boundary checks in fused MoE and architecture-specific FP4 quantization library refactors for SM90/SM100 to improve compatibility and stability. These changes are designed to increase throughput for large MoE models, expand hardware support, and reduce risk in production deployments.

July 2025 monthly summary for bytedance-iaas/vllm. Implemented GPU-accelerated GEMM improvements for SM100 with FP8, delivering performance gains and enabling smaller batch sizes. Also applied a stability fix to maintain compatibility with activation functions and input conditions by disabling the Cutlass Block Scaled Group GEMM in expert parallelism mode. Result: higher throughput on SM100 FP8 paths, lower latency, and more robust, maintainable execution across workflows. Technologies involved include CUTLASS, SM100, FP8, and group GEMM optimizations.