How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention

Summary

<p>We implement xFormers, a practical toolkit for fast, memory-efficient Transformer models on GPUs. We validate memory-efficient attention against a standard implementation, then compare speed and memory across sequence lengths. We work through causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi biases. Finally, we combine these into a trainable GPT-style model with SwiGLU layers and automatic mixed-precision training.</p> <p>The post <a href="https://www.marktechpost.com/2026/06/16/how-to-build-memory-efficient-transformers-with-xformers-using-packed-sequences-gqa-alibi-swiglu-and-causal-attention/">How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>