AMDs deep learning libraries are very bad the last time I checked, nobody uses amd in that space for that reason. Nvidia has a quazi monopoly, that's the main reason for the price difference IMHO.
this...
nearly 95% of deeplearning github repos are "tested using cuda gpu - others, not so sure"
the only way to out-run nvidia is to have 3~10x better bang-for-buck.
Or AMD can just provide a "DIY unlimited gpu RAM upgrade" kit -- a lot of people are buying macstudio 128gb ram because of its "bigger ram-for-buck" than nvidia gpus
>This has advantages in practical programming. It allows you to write independent code >fragments as data which can be passed to other parts of the program to be later executed >as code in that context.
This amuses me because while its technically true this amazing feat is accomplished only by denuding of the code of substantial expressive power - namely the relation between the lexical denotation of the code and its meaning. I will say this - aesthetically, I prefer picolisp's approach to Common Lisp's, which is to just paper over this problem with gensyms, packages, etc. Give me hygienic macros or give me death.