# Mixed Precision Training

Using numerical formats that have lower precision than the common 32-bit floating point has several benefits:

- They require less memory, lowering the footprint for training and deployment
- They require less memory bandwidth, speeding up data transfers
- Math operations run faster in reduced precision

Mixed-precision training offers computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network.

The Volta generation of GPUs have Tensor Cores, which provide 8x more throughput than single-precision math pipelines. Each Tensor Core performs \(D = A \times B + C\), where \(A\) and \(B\) are half-precision 4x4 matrices, while \(D\) and \(C\) can be either half or single-precision matrices.

One caveat is that some networks require their gradient values to be shifted or scaled to match the accuracy of single-precision training.