*My transformer in PyTorch has 14 million parameters in float32. How much memory do I need for inference on a CPU to process a batch of 2 with a sequence length of 40?*

To calculate the memory needed for inference on a CPU for your PyTorch Transformer model with 14 million parameters in float32, you need to consider several components:

- Model Parameters: You mentioned the model has 14 million parameters, and each parameter is a float32 (4 bytes). The memory needed for the model parameters would be 14×106×414×106×4 bytes.
- Intermediate Outputs: Each layer in the Transformer network would produce intermediate outputs. For a batch of 2 and a sequence length of 40, the output size would be
`[Batch_Size, Sequence_Length, Hidden_Dim]`

for each layer. If your model has*L*layers, you need to account for*L*such outputs. - Input Tensors: The input tensor size will be
`[Batch_Size, Sequence_Length, Input_Dim]`

, which also consumes memory. - Additional Memory: Some extra memory is used for the computational graph, gradients (though not for inference), and other overheads.

Let’s assume:

*L*is the number of layers in your Transformer*H*is the hidden dimension size*I*is the input dimension size*O*is the output dimension size (usually same as*H*)

Memory for intermediate outputs for *L* layers:

*L*×[2×40×*H*]×4 bytes

Memory for input tensors:

[2×40×*I*]×4 bytes

Memory for output tensor:

[2×40×*O*]×4 bytes

Total Memory needed:

Model Parameters+Intermediate Outputs+Input Tensors+Output Tensor+Additional Memory

Let’s calculate this, assuming *L*=12, *H*=768, *I*=768, *O*=768, and an additional memory overhead of 10%.

`# Given values`

num_parameters = 14_000_000 # 14 million

float_size = 4 # float32 is 4 bytes

batch_size = 2

seq_length = 40

hidden_dim = 768 # example hidden dimension

input_dim = 768 # example input dimension

output_dim = 768 # example output dimension

num_layers = 12 # example…