Comparison
Mixed Precision vs Pretraining
Mixed Precision and Pretraining are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Mixed Precision
Mixed Precision comes up when the question is fundamentally about infrastructure.
Pretraining a 7B model in BF16 instead of FP32.
When you would reach for Pretraining
Pretraining comes up when the question is fundamentally about training.
GPT-3 pretrained on ~300B tokens.
Frequently asked
What is the difference between Mixed Precision and Pretraining?
Mixed Precision: Mixed-precision training does the bulk of forward and backward computation in 16-bit floats (BF16 or FP16) while keeping master weights and certain accumulations in 32-bit. Faster, smaller, same accuracy. Pretraining: Pretraining is the initial training phase where an LLM learns to predict the next token on trillions of tokens of general text. It produces a base model that can be adapted later.
When should I use Mixed Precision vs Pretraining?
Mixed Precision is the right concept when you are focused on infrastructure. Pretraining applies when you are focused on training.
Are Mixed Precision and Pretraining the same thing?
No. Mixed Precision is infrastructure; Pretraining is training. They are related but address different parts of the AI stack.