Yes and no. I wouldn't do mid/low storage tiers in a GPU b/c indeed, drinking through a straw. When it's all I/O, even the insane GPU bandwidth still assumes e

Yes and no. I wouldn't do mid/low storage tiers in a GPU b/c indeed, drinking through a straw. When it's all I/O, even the insane GPU bandwidth still assumes enough compute to go with it. A couple of GPU vendors pitch themselves as GPU DBs, and that's tough positioning when the assumption is all the data lives in the DB. From what I can tell, that only works for < TB in practice, and ideally < 10GB with few concurrent users. But if you're doing a lot of Spark/Impala/Druid style compute, where storage is probably separate anyways (parquets in HDFS/S3 -> ...) and there is increasingly math to go along with it (analytics, ML, neural nets, data viz, ...), different story. Now that stuff like regex is pretty easy with RAPIDS, instead of doing pandas -> spark or pandas -> rapids, I try to start with cudf to beginwith. (But definitely still not quite there.) We partner a bunch with BlazingSQL here, and they've always been chasing the out-of-core story here. A couple of the lesser-known GPU 'DB's do as well, such as FastData focusing explicitly on replacing spark/flint wrt both batch & streaming. A few trends you may want to reexamine the #s on: -- CPU perf/watt (~= perf/$) vs GPU perf/watt (~= perf/$), especially in cloud over last 10 years: GPU is steadily dropping while CPU isn't -- CPU-era Spark and friends are increasingly bound by network I/O, while GPU boxes go for thicker. You can also do Spark on a thicker box, but at that point, might as well go shared GPU and keep it there (RAPIDS) -- Nvidia & cloud providers have been pushing on direct-to-gpu and direct gpu<>gpu, including at commodity levels. Mellanox used to be a problem there, and now they control them. My guess is the bigger challenge in ~2yr will be rewriting RAPIDS for streaming & serverless & more transparent multi-GPU; the HW is hard but seems more predictable and much better staffed. GPU isn't an end-all, but when a lot of CPU data libs are going data parallel / columnar, and Nvidia is improving more than Intel for perf/watt (= perf/$), the choice between multicore x SIMD vs GPU keeps tilting in Nvidia's favor.