From direct discussions with the sklearn team, note that this may change relatively soon: a GPU engineer funded by Intel was recently added to the core development team. Last time I met with the team in person (6 months ago), the project was to factor some of the most GPU friendly computations out of the sklearn code base, such as K-Nearest Neighbor search or kernel-related computations, and to document an internal API to let external developers easily develop accelerated backends. As shown by e.g. our KeOps library, GPUs are extremely well suited to classical ML and sklearn is the perfect platform to let users fully take advantage of their hardware. Let’s hope that OP’s question will become redundant at some point in 2023-24 :-)