  We show that quantum computers can be used for producing large $n$-partite
nonlocality, thereby providing a method to benchmark them. The main challenges
to overcome are: (i) The interaction topology might not allow arbitrary
two-qubit gates. (ii) Noise limits the Bell violation. (iii) The number of
combinations of local measurements grows exponentially with $n$. To overcome
(i), we point out that graph states that are compatible with the two-qubit
connectivity of the computer can be efficiently prepared. To mitigate (ii), we
note that, for specific graph states, there are $n$-partite Bell inequalities
whose resistance to white noise increases exponentially with $n$. To address
(iii) for any $n$ and any connectivity, we introduce an estimator that relies
on random sampling. As a result, we propose a method for producing $n$-partite
Bell nonlocality with unprecedented large $n$. This allows in return to
benchmark nonclassical correlations regardless of the number of qubits or the
connectivity. We test our approach by using a simulation for a noisy IBM
quantum computer, which predicts $n$-partite Bell nonlocality for at least
$n=24$ qubits.
