Unlikely. Even with turn costs enabled 256GB (or less) are sufficient. You could also try to disable CH as for bike often no long routes are required (you could disable them). Here we have written down a few more details: https://www.graphhopper.com/blog/2022/06/27/host-your-own-wo...
I thought I would be able to compute the graph with 64GB of ram but it kept crushing before CH and LM stage. After switching to a 128GB instance, it finally worked, hitting around 90GB at peak memory usage. For context, I was using 3 profiles - one with CH and two with LM, plus elevation data and used all of the tips from deploy.md
Yes, definitely.
> I thought I would be able to compute the graph with 64GB of ram but it kept crushing before CH and LM stage.
For normal GraphHopper and just the EU the 64GB should be more than sufficient.
Maybe you already considered, but there are a number of collection libraries out there that are optimized for holding Java primitives and/or for very large sets of data, which could help you save significant memory. Eclipse Collections [0] and Fastutil [1] come to mind first, but there are many out there [2]
[0] https://github.com/eclipse-collections/eclipse-collections [1] https://fastutil.di.unimi.it/ [2] https://github.com/carrotsearch/hppc/blob/master/ALTERNATIVE...
Another trick for planet size data structure could be to use a List instead of the Map and the OSM ID as index. Because the memory overhead of a Map compared to a List is huge (and you could use DataAccess) and the OSM IDs for planet are nearly adjacent or at least have not that many gaps (as those gaps are refilled I think).
All these tricks (there are more!) are rather tricky&low level but necessary for memory efficiency. A simpler way for your use case could be to just use a database for that, like MapDB or sqlite. But this might be (a lot) slower compared to in-memory stuff.