# Define your own parallel model¶

Let’s say that you have a huge MLP model with billions of parameters and its extremely large hidden layer size makes it impossible to fit into a single GPU directly. Don’t worry, ColossalAI is here to help you sort things out. With the help of ColossalAI, you can write your model in the familiar way in which you used to write models for a single GPU, while ColossalAI automatically splits your model weights and fit them perfectly into a set of GPUs. We give a simple example showing how to write a simple 2D parallel model in the Colossal-AI context.

## Write a simple 2D parallel model¶

from colossalai.nn import Linear2D
import torch.nn as nn

class MLP_2D(nn.Module):

def __init__(self):
super().__init__()
self.linear_1 = Linear2D(in_features=1024, out_features=16384)
self.linear_2 = Linear2D(in_features=16384, out_features=1024)

def forward(self, x):
x = self.linear_1(x)
x = self.linear_2(x)
return x


## Use pre-defined model¶

For the sake of your convenience, we kindly provide you in our Model Zoo with some prevalent models such as BERT, VIT, and MLP-Mixer. Feel free to customize them into different sizes to fit into your special needs.