# SGD

Stochastic gradient descent (SGD) is a basic gradient descent optimizer to minimize loss given a set of model parameters and updates the parameters in the opposite direction of the gradient. The update is performed on a randomly sampled mini-batch of data from the dataset.

bitsandbytes also supports momentum and Nesterov momentum to accelerate SGD by adding a weighted average of past gradients to the current gradient.

## SGD[[api-class]]

[[autodoc]] bitsandbytes.optim.SGD
    - __init__

## SGD8bit

[[autodoc]] bitsandbytes.optim.SGD8bit
    - __init__

## SGD32bit

[[autodoc]] bitsandbytes.optim.SGD32bit
    - __init__