Precision Settings

➺ Key Components:

  • Floating-point precision (FP32, FP16, BF16)
  • Mixed precision training
  • Quantization strategies 

    • ➺ Technical Details:

    FP32 (32-bit floating point) 
  • Standard precision level
  • Higher memory usage but maximum accuracy
  • Best for initial model development 
    • FP16 (16-bit floating point)
  • Half-precision format
  • Reduces memory usage by ~50%
  • Faster training on modern GPUs
  • Requires careful scaling to prevent underflow/overflow
    • BF16 (Brain Floating Point) 
  • Google’s 16-bit format
  • Better numeric range than FP16
  • Popular in TPUs and modern AI accelerators 

    • ➺ Practical Applications:

  • Production deployment strategies
  • Memory optimization techniques 
  • Accuracy-performance tradeoffs 

    • ➺ Best Practices:

  • Start with FP32 for initial development
  • Use mixed precision for training
  • Consider hardware constraints
  • Monitor for numerical stability