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As language models become more general pur- pose, increased attention needs to be paid to detecting out-of-distribution (OOD) instances, i.e., those not belonging to any of the distribu- tions seen during training. Existing methods for detecting OOD data are computationally complex and storage-intensive. We propose a novel soft clustering approach for OOD detec- tion based on non-negative kernel regression. Our approach greatly reduces computational and space complexities (up to 11× improve- ment in inference time and 87% reduction in storage requirements). It outperforms existing approaches by up to 4 AUROC points on four benchmarks. We also introduce an entropy- constrained version of our algorithm, leading to further reductions in storage requirements (up to 97% lower than comparable approaches) while retaining competitive performance. Our soft clustering approach for OOD detection highlights its potential for detecting tail-end phenomena in extreme-scale data settings. Our source code is available on Github.