The benefits would come from running query kernels in parallel on thousands of GPU cores simultaneously, which necessarily only have access to memory on the car

The benefits would come from running query kernels in parallel on thousands of GPU cores simultaneously, which necessarily only have access to memory on the card. Of course pre-computation is always preferable, but that's not feasible for data exploration and many types of queries. My guess is they start with databases or i dexes that can sit entirely in GPU RAM, and then later versions will support streaming this data from main memory and disk. Some types of queries that would likely benefit from this strategy would be reductions (sum, avg, count), filtering (find items matching a particular value or predicate), and geospatial queries against geometry. These all require iterating over all or most of the data, and so doing that massively parallel will result in major speedups. To support larger datasets you can always shard across GPUs and machines. This is how the google index is structured, for example. I bet real-time metrics will be a good application.