🟢 What are 3 key ideas?
1️⃣ Using Transformer Engine replaces standard blocks with optimized versions: less memory, faster matrix operations, support for FP8/FP4. This immediately increases training and inference speed.
2️⃣ Scale training to billions of parameters
Through FSDP and hybrid parallelism modes, the model can be distributed across multiple GPUs or nodes. And most importantly, the configuration is already ready, no need to assemble everything manually.
3️⃣ Save memory through sequence packing
Usually, biological sequences vary greatly in length, and half of the batch is filled with paddings. Packing allows you to "compress" the batch by removing empty tokens, resulting in higher speed and less VRAM usage.
No one wants to write CUDA kernels manually. BioNeMo Recipes allow you to use the familiar PyTorch + HuggingFace stack while achieving performance at the level of "big" frameworks.
#NVIDIA
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