What is computational complexity of $Ax=b$ when size of A increasing

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I have a linear equation

$$Ax=b$$

where $A$ is non-singular matrix $N \times N$, $x,b$ are vector $N\times 1$, $A,b$ are given and I want to find $x$

It is clear that $x$ can find by $x=A^{-1}b$. I would like to know how computational complexity increasing when $N$ is increasing (you can use any method to find solution)? Thank you in advance

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The exact methods for a $n \times n$ matrix need at least $n^2$ steps, the present theoretical boundary seems to be $O(n^{2.376})$ (source). Gauss elimination needs $O(n^3)$ (source).

There are approximative methods, like Gauss-Seidel which are faster.

And there are methods which have advantages for certain matrices.