  Random K-out graphs are receiving attention as a model to construct sparse
yet well-connected topologies in distributed systems including sensor networks,
federated learning, and cryptocurrency networks. In response to the growing
heterogeneity in emerging real-world networks, where nodes differ in resources
and requirements, inhomogeneous random K-out graphs, denoted by $H(n;\mu,K_n)$,
were proposed recently. Motivated by practical settings where establishing
links is costly and only a bounded choice of $K_n$ is feasible ($K_n = O(1)$),
we study the size of the largest connected sub-network of $H(n;\mu,K_n)$, We
first show that the trivial condition of $K_n \geq 2$ for all $n$ is sufficient
to ensure that $H(n;\mu,K_n)$, contains a giant component of size $n-O(1)$ whp.
Next, to model settings where nodes can fail or get compromised, we investigate
the size of the largest connected sub-network in $H(n;\mu,K_n)$, when $d_n$
nodes are selected uniformly at random and removed from the network. We show
that if $d_n=O(1)$, a giant component of size $n- \OO(1)$ persists for all $K_n
\geq 2$ whp. Further, when $d_n=o(n)$ nodes are removed from $H(n;\mu,K_n)$,
the remaining nodes contain a giant component of size $n(1-o(1))$ whp for all
$K_n \geq 2$. We present numerical results to demonstrate the size of the
largest connected component when the number of nodes is finite.
