What is the memory usage difference between FP16 and BF16?

Memory Usage Comparison Between FP16 and BF16 Formats

When comparing FP16 (Half Precision) and BF16 (Brain Floating Point 16-bit), it's important to note that both numerical formats occupy the same amount of memory per element—16 bits (2 bytes). As a result, the memory footprint of FP16 and BF16 is identical when storing data.

However, there are key differences in precision and application use cases between these two formats.

FP16 vs. BF16: Key Differences Explained

FP16 (IEEE 754 Half Precision)

• Memory Usage: 16 bits (2 bytes) per value.
• Structure: FP16 allocates 1 bit for sign, 5 bits for exponent, and 10 bits for mantissa (fractional bits).
• Precision: Higher precision due to more mantissa bits.
• Use Case: Commonly used for deep learning inference and training where hardware acceleration supports FP16 arithmetic (e.g., NVIDIA GPUs with Tensor Cores).

BF16 (Brain Floating Point 16)

• Memory Usage: Also 16 bits (2 bytes) per value.
• Structure: BF16 allocates 1 bit for sign, 8 bits for exponent, and 7 bits for mantissa.
• Precision: Lower precision in mantissa (fractional bits) than FP16, but higher dynamic range due to more exponent bits.
• Use Case: Widely adopted in AI and deep learning training (especially in large-scale models, such as those used in transformer architectures) due to its higher dynamic range and reduced complexity in hardware implementation.

Memory Usage Example: FP16 vs. BF16

Suppose you have a neural network model with 1 million parameters. Here's how the memory usage compares:

• FP16: 1,000,000 parameters × 2 bytes per parameter = 2 MB
• BF16: 1,000,000 parameters × 2 bytes per parameter = 2 MB

Both FP16 and BF16 have exactly the same memory footprint, 2 MB in this example.

When to Use FP16 or BF16?

Choose FP16 When:

• You require higher numeric precision in mantissa bits.
• Your hardware (e.g., NVIDIA GPUs) efficiently supports FP16 arithmetic with Tensor Cores.

Choose BF16 When:

• You need a higher dynamic range (useful for gradients and activations during training in modern neural networks).
• You are working on hardware platforms supporting BF16 (e.g., Intel CPUs, TPUs, newer-generation GPUs like NVIDIA Ampere/Hopper GPUs).

Summary Table: FP16 vs. BF16

Conclusion

FP16 and BF16 have the same memory usage (16 bits per value), but differ in the allocation of bits between exponent and mantissa. FP16 offers higher precision, while BF16 provides greater dynamic range, making each suitable for different deep learning scenarios and hardware architectures.