This paper studies a scalable approach for ordinary least squares (OLS) regression based on distributed sketching. It distributes small sketches of a large data set across multiple workers, has each worker construct a local OLS estimator from its sketch, and averages the local estimators into a global one. Prior studies assume each sketch is generated from all data, but this sketching step has high computation cost, sometimes even higher than standard OLS. Instead, we propose to generate each sketch from a partitioned subset of the data, which allows the sketching cost to decrease with more workers. Under the fixed design setting, we characterize the exact excess loss of the global estimator. This loss is higher than the loss established in prior studies, but remains close to it when the average Burg divergence between subset covariances is small. Since more workers tend to increase the divergence under our partition design, this reveals a tradeoff between the computational and statistical efficiency of our estimator.

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