cutlass

This paper presents CUTLASS-based kernels that fuse SwiGLU activation with GeMM at the tile level, achieving up to 2.47× speedup on NVIDIA H100 for efficient LLM inference.

The author, as part of a 100-part GPU learning series, reviews a GTC 2020 lecture by Andrew Kerr on developing high-performance CUDA kernels for Tensor Cores on NVIDIA A100, discussing techniques and the trade-off between raw CUDA and CUTLASS.

A developer discovered that vLLM only used one of two matmul engines on the RTX Pro 6000 for Qwen3.6 27B models. A plugin by Fable 5 selects the right engine per call, nearly doubling prefill performance.

vLLM v0.22.0 released with 459 commits, featuring DeepSeek V4 hardening, experimental Rust frontend, and batch-invariant Cutlass FP8, reducing end-to-end latency by 28.9%.

New articles in the GPU Glossary cover CuTe DSL, CUTLASS, and CuTe – tools for writing high-performance GPU kernels on data center GPUs, with examples in Python.

MoonshotAI released FlashKDA, open-source CUTLASS kernels for Kimi Delta Attention that deliver up to 2.22x speedup over Triton on H20 GPUs.

Moonshot AI releases FlashKDA, an open-source CUTLASS-based implementation of Kimi Delta Attention kernels that delivers 1.72×–2.22× prefill speedup on H20 GPUs.

Discussion of the shift in GPU kernel engineering from C++ CuTe/CUTLASS to NVIDIA's Python-based CuTeDSL, questioning whether new engineers should learn legacy C++ templates or prioritize the emerging stack for LLM inference work.

FlashKDA is a high-performance implementation of Kimi Delta Attention (KDA) kernels built on CUTLASS, optimized for SM90+ GPUs and integrated with flash-linear-attention.