Transformer architectures are the backbone of many modern large language and generative AI models. As these models grow in size, training runs consume more GPU hours and more engineering iteration time. Accelerating transformers is therefore not just a performance optimization, but directly affects how quickly teams can experiment and how large a model they can afford to train. NVIDIA Hopper and NVIDIA Blackwell GPUs help solve this problem by introducing low-precision operator support including FP8 and NVFP4.
Transformers spend much of their training time in GEMMs, and low-precision formats speed up training mainly by making those matrix multiplications faster and cheaper. However, your transformer config does not tell you which GEMMs are actually running in your model. If you want to understand where training time goes, you need to turn your transformer config and batch size into the exact M×K×N matrix shapes your model executes, then benchmark those shapes across precisions. This will help you determine the optimal precision for your architecture before committing to a more expensive training run.
NVIDIA Transformer Engine (TE) can handle quantization and kernel dispatch unlocking low precision formats. This post shows you how to move from high-level model settings to concrete GEMM workloads, profile them with a microbenchmark, and estimate where lower precision will actually translate into speedups to help you accelerate your transformer-based models. The use case features CodonFM, a language model for biology focused on RNA.
