Mixed Precision Training
Definition
Mixed Precision Training combines different numerical formats for model training.
Training with multiple numerical formats, typically lower precisions for speed and FP32 for sensitive calculations or states.
Simply explained
How to use tools of varying accuracy: coarse where sufficient, and more precise for sensitive steps.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
Matrix operations run in BF16 while optimizer states remain higher precision.
Limits and pitfalls
- Lower numerical precision can make calculations unstable or cause very small values to vanish. Differences in the hardware used must be considered.
Origin & history
Mixed-precision methods have become widespread with specialized GPU units and loss scaling.
Use in mid-sized companies
- Possible application: A development team sets numerical formats for training steps. It monitors stability and result quality on the intended hardware against a reference.
Frequently asked questions
What does training with mixed precision mean?
Training with multiple numerical formats, typically lower precisions for speed and FP32 for sensitive calculations or states.
Which mistakes should be avoided?
Not monitoring numerical instability, underflow, and hardware differences.
Sources
Source checked on 10.10.2026. Editorial use cases are hypothetical; no proof of real customer results.
Editorially reviewed on: