What is the Memory Usage Difference Between FP16 and BF16?

Vishnu Subramanian

Founder @JarvisLabs.ai

Both FP16 and BF16 use 16 bits of memory per value, but BF16 allocates more bits to the exponent (8) and fewer to the mantissa (7) compared to FP16 (5 for exponent, 10 for mantissa). This makes BF16 better for training deep learning models despite lower precision, while FP16 offers better precision for specific inference workloads.

FP16 vs BF16: Memory Usage Comparison

Both FP16 and BF16 use exactly 16 bits per value, so there's no difference in memory usage or storage requirements between them. Both formats reduce memory usage by 50% compared to FP32 (32-bit floating point).

Key differences in bit allocation

Practical implications

While memory usage is identical, the formats have different strengths:

• FP16 (IEEE 754 Half Precision): Better precision for smaller magnitude values
• BF16 (Brain Floating Point): Better numerical stability with a larger dynamic range (similar to FP32)

BF16 is often preferred for training neural networks because it maintains the same exponent range as FP32, making it more numerically stable despite having lower precision than FP16. While both formats use identical memory, their performance characteristics can significantly impact your model's convergence speed and final accuracy.