Bfloat

Key Features

Bfloat.ai Bfloat is a special number format for deep learning. It has one sign bit, eight exponent bits, and seven mantissa bits. This design gives it a bigger range than the usual float16 format, matching the range of float32. Bfloat16 is used in automatic mixed precision training. This training mixes single precision (float32) and half precision (float16 or bfloat16) to boost performance.

Benefits

Using bfloat16 has many plus points. It makes training faster and uses less memory, saving space while keeping accuracy. Bfloat16 also has fewer problems with underflows, overflows, or other number issues during training compared to float32. This is because it has the same exponent size as float32, avoiding the issues seen with float16. Plus, bfloat16 needs fewer bits to store weights, saving space during training and when saving checkpoints.

Use Cases

Bfloat16 is great for training big language models like GPT-3 XL. Tests show that using bfloat16 speeds up training by 18% compared to float16. It also has less weight growth, which can mean the model is unstable or overfitting. Models trained with bfloat16 score better in tests, showing they work better on new data.

Cost/Price

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Funding

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Reviews/Testimonials

Tests with different deep learning networks, including the GPT-3 XL network, show that using bfloat16 speeds up training by 18% compared to float16. Also, bfloat16 has less weight growth, which can mean the model is unstable or overfitting. Models trained with bfloat16 also score better in tests, showing they work better on new data.