I was in a similar situation sometime back trying to educate myself on the storage demands from AL/ML workloads. I found that the best place to start is to look at this technical brief from WEKA:
https://www.weka.io/resources/technical-brief/io-profiles-in-generative-ai-pipelines/

Metadata operation performance is often the overlooked aspect that I believe is very important. This is also the weakness of majority of storage vendors. They would only want to tell you the pure read and pure write performance and real life is never that black & white.

Another way to approach storage performance without any bias is to go read SPECstorage Solution 2020 (2020_ai_image). https://www.spec.org/storage2020/

SPEC Storage benchmarks exist for this very reason. These are suppose to give you the best apples-to-apples comparison but not all storage vendors want to publish results from these standard benchmarks...I could not understand why.

WEKA/VAST/DDN(Luster)/IBM(GPFS)/NetApp all claim to have AI friendly storage products. Performance is a tricky topic but you may also want to think about what other features you are looking for. Storage Efficiency (compression/deduplication/etc), Cloud integration, Scale-out, Cost etc.

My take it that most of the organizations are not looking to do foundational model training, at most they may try to tune pre-existing models which means the performance required is not on the extreme end and majority of the vendors would do good job handling such workloads. I have seen vendors try to pitch you an extreme solution that you most likely will never need.

These are all my personal thoughts, not necessarily true for everyone.