For any given metric space, obtaining an offline optimal solution to the classical k-server problem can be reduced to solving a minimum-cost partial bipartite matching between two point sets A and B within that metric space. For d-dimensional 𝓁_p metric space, we present an Õ(min{nk, n^{2-1/(2d+1)}log Δ}⋅ Φ(n)) time algorithm for solving this instance of minimum-cost partial bipartite matching; here, Δ represents the spread of the point set, and Φ(n) is the query/update time of a d-dimensional dynamic weighted nearest neighbor data structure. Our algorithm improves upon prior algorithms that require at least Ω(nkΦ(n)) time. The design of minimum-cost (partial) bipartite matching algorithms that make sub-quadratic queries to a weighted nearest-neighbor data structure, even for bounded spread instances, is a major open problem in computational geometry. We resolve this problem at least for the instances that are generated by the offline version of the k-server problem. Our algorithm employs a hierarchical partitioning approach, dividing the points of A∪ B into rectangles. It maintains a partial minimum-cost matching where any point b ∈ B is either matched to another point a ∈ A or to the boundary of the rectangle it is located in. The algorithm involves iteratively merging pairs of rectangles by erasing the shared boundary between them and recomputing the minimum-cost partial matching. This continues until all boundaries are erased and we obtain the desired minimum-cost partial matching of A and B. We exploit geometry in our analysis to show that each point participates in only Õ(n^{1-1/(2d+1)}log Δ) number of augmenting paths, leading to a total execution time of Õ(n^{2-1/(2d+1)}Φ(n)log Δ). We also show that, for the 𝓁₁ norm and d dimensions, any algorithm that can solve instances of the offline n-server problem with an exponential spread in T(n) time can be used to compute minimum-cost bipartite matching in a complete graph defined on two (d-1)-dimensional point sets under the 𝓁₁ norm within T(n) time. This suggests that removing spread from the execution time of our algorithm may be difficult as it immediately results in a sub-quadratic algorithm for bipartite matching under the 𝓁₁ norm.