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The bfloat16 quantization encoding.

Like float32, the bfloat16 encoding uses 8 bits to store the exponent value, so it has the same numeric range as float32. However, it has just 7 bits for the mantissa (compared to 23 bits available in float32), so it has less precision for the fractional part. This is often a better trade-off for ML applications, compared to traditional float16, which has less numeric range because it uses only 5 bits to store the exponent (though it has better precision with 10 bits for the mantissa).

Implemented traits

AnyType, QuantizationEncoding



static quantize(tensor: Tensor[float32]) -> Tensor[uint8]

Quantizes the full-precision input tensor to bfloat16.

Only supports quantizing from float16 and float32, using a direct elementwise cast.


  • tensor (Tensor[float32]): Full-precision tensor to quantize to bfloat16.


Quantized bfloat16 tensor. The tensor datatype is uint8 because this is simply a byte buffer. Each scalar is actually encoded into two bytes (16-bits).


static id() -> String

Identifier for the bfloat16 quantized encoding.