xFormers tutorial shows how to build memory-efficient Transformer pipelines

A new technical guide walks through building memory-efficient Transformer models with xFormers. The tutorial covers packed variable-length sequences, grouped-query attention, ALiBi biases, SwiGLU layers, causal masking, and mixed-precision training.

The topic matters because memory and throughput remain practical limits for training and serving Transformer models. Techniques that reduce waste can make longer contexts and larger batches more accessible on available GPUs.

For practitioners, the guide is a hands-on example of how modern efficiency components fit together in a GPT-style model pipeline.