Tutorial: Accelerating Transformer Training with NVIDIA Transformer Engine

A new technical tutorial explores how NVIDIA Transformer Engine accelerates transformer workloads by combining fused GPU kernels, BF16 computation, and hardware-aware FP8 execution. The tutorial demonstrates capabilities by combining fused components such as te.Linear, te.LayerNorm, te.LayerNormMLP, and te.TransformerLayer while configuring a delayed-scaling FP8 strategy. The workflow detects the active GPU architecture to determine support for TE kernels, FP8 tensor cores, or pure PyTorch fallback. A compact GPT-style causal language model is constructed using te.TransformerLayer blocks, trained on deterministic synthetic sequences, and comparing higher-precision versus FP8 execution. Benchmarks measure forward propagation, backpropagation, and optimizer updates using higher-precision and FP8 modes, quantifying performance and memory impact. Scaling factors and amax history maintained by Transformer Engine are inspected to understand how delayed scaling stabilizes FP8 tensors. Greedy autoregressive generation validates whether the model preserves training patterns, testing compatibility across different Colab GPU environments and demonstrating practical integration of optimizations with automatic fallback to BF16 or FP32.