MiniMax Sparse Attention (MSA): a Two-Branch Block-Sparse Attention Trained on a 109B-Parameter MoE With — 2026-06-17

Impact Vector: AI Tools · Jun 17, 2026 · 3m

## Short Segments Building memory-efficient Transformers just got easier with xFormers, a toolkit for fast, memory-efficient models on GPUs. Today, we'll explore how xFormers combines packed sequences, GQA, ALiBi, SwiGLU, and causal attention to optimize Transformer models. Coming up, we'll dive into MiniMax's new sparse attention method, which promises to revolutionize long-context AI models. Memory-efficient Transformers are now within reach thanks to xFormers, a practical toolkit for building fast models on GPUs. This tutorial demonstrates how xFormers validates memory-efficient attention…