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 # Tensor Parallelism
 
-Tensor parallelism is a technique used to fit a large model in multiple GPUs. For example, when multiplying the input tensors with the first weight tensor, multiplying both tensors is equivalent to splitting the weight tensor column-wise, multiplying each column with input separately, and then concatenating the separate outputs. These outputs are then sent between GPUs and then concatenated together to get the final result, like below 👇 
+Tensor parallelism is a technique used to fit a large model in multiple GPUs. For example, when multiplying the input tensors with the first weight tensor, the matrix multiplication is equivalent to splitting the weight tensor column-wise, multiplying each column with the input separately, and then concatenating the separate outputs. These outputs are then transferred from the GPUs and concatenated together to get the final result, like below 👇 
 
 ![Image courtesy of Anton Lozkhov](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/TP.png)