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ArrowMetal is now on Apache Arrow's Powered By page and on Apache DataFusion's integrations list. What the two listings mean, and what the listed thing does as of 0.5.0, with one real run on each side.
Polars 2.0.0 shipped on 6 October and runs its group-bys, joins and sorts faster than 1.44. Two days later ArrowMetal's MetalEngine was re-measured against it on an Apple M4 Max: the GPU default takes 52 of 107 queries at 50 million rows, each 1.35x to 7.94x faster than the faster Polars 2.0 engine, and hands 12 back that it used to take. What Polars 2.0 changed, and how a crossover table answers it.
I built ArrowMetal, the first Apache Arrow project I could find that runs compute on the Apple silicon GPU. Apple silicon has one memory for CPU and GPU, and an Arrow buffer in shared Metal memory is already a GPU buffer; no Arrow project used that. ArrowMetal does: 307 of Arrow's 307 compute functions, seven languages, take at 24.2x pyarrow on an M4 Max, and 339 benchmark rows against the fastest CPU idiom of Polars, pyarrow, pandas and numpy, including the 77 where the CPU is still ahead.