Paper Detail
Gal Bloch, Ariel Gera, Matan Orbach, Ohad Eytan, Assaf Toledo
We present Flash-GMM, a fused Triton kernel for efficient computation of Gaussian Mixture Models (GMMs) over large-scale data in a single GPU pass. By eliminating the need to materialize the full responsibility matrix in GPU memory, Flash-GMM achieves a 20times speedup over existing implementations and enables training on datasets more than 100times larger than previously feasible on one device. To demonstrate its impact, we integrate Flash-GMM into the IVF coarse quantizer for approximate nearest-neighbor (ANN) search. We show that soft GMM clustering is now a viable drop-in replacement for k-means, and that GMM responsibilities can be leveraged to assign border vectors to multiple clusters. Our approach reaches fixed recall targets with up to 1.7times fewer distance computations, or equivalently, yields +2--12 recall@10 at matched computational cost. We release the kernel as an open-source project.
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@misc{bloch2026flash,
title = {Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering},
author = {Gal Bloch and Ariel Gera and Matan Orbach and Ohad Eytan and Assaf Toledo},
year = {2026},
abstract = {We present Flash-GMM, a fused Triton kernel for efficient computation of Gaussian Mixture Models (GMMs) over large-scale data in a single GPU pass. By eliminating the need to materialize the full responsibility matrix in GPU memory, Flash-GMM achieves a 20times speedup over existing implementations and enables training on datasets more than 100times larger than previously feasible on one device. To demonstrate its impact, we integrate Flash-GMM into the IVF coarse quantizer for approximate neare},
url = {https://huggingface.co/papers/2606.10896},
keywords = {Gaussian Mixture Models, Triton kernel, responsibility matrix, approximate nearest-neighbor search, k-means, soft clustering, IVF coarse quantizer, distance computations, recall@10, code available, huggingface daily},
eprint = {2606.10896},
archiveprefix = {arXiv},
}
{}