The downvotes for you are really disappointing. Because before AI, there was the walled garden of HPC, with temple-like supercomputers to which gaining access

The downvotes for you are really disappointing. Because before AI, there was the walled garden of HPC, with temple-like supercomputers to which gaining access involved magic incantations and politically savvy groveling from academics wishing to graduate from grad school in less than a decade. GPUs disrupted that garden in a big way. Or all of this has happened before(tm). But in the past few years, the one two punch of AI and Python rebuilt that garden. So now not only are the big guys controlling training the big AIs, but the insistence on describing those computations entirely from a Python abstraction of those computations is leading to insanely inefficient use of those GPUs (and coming soon, dedicated ASICs with even worse software libaries past Resnet, BERT and a few other acceptable strawmen). That said, one can do a lot with AI/ML models that fit in a single sub $10,000 machine built entirely from consumer parts and doubly so if one is willing to profile and low-level optimize one's code as intensely as stare at the data going into the model (you're in grad school, you have time for this, you have all the time so to speak and it will save you lots of time in the long run). For inspiration, all the big guys have GPU codemonkeys on staff to micro-optimize as needed. One might want to take a cue from that and DIY.