Those suggestions they make for a B200 start at $4.99 an hour.
Is that really required, for starting out? I've been tinkering with my own from-scratch LLM, but in the early phases I don't need anything more than a 4090 on Vast.ai
We were lucky enough to get Blackwell GPUs for Stanford students this year, which is why the writeups are written mostly around them.
- the hardware you need for a production use-case is relatively small, because production {models, bitstreams} have been heavily size-optimized, stripping out everything not needed to get a good result for the target use-cases
- but the hardware you need when tinkering/learning how to design {compute kernels, IP blocks} in the first place, must be quite a bit more powerful / higher-capacity, because your experiments will intentionally be the opposite of optimized: they'll be built for legibility / introspectability / debuggability at every level, which massively inflates and de-optimizes the resulting {model, bitstream}.
(And, to be clear here, "running someone else's finished model, which was designed and optimized to be used on something like a 4090, against your own prompt" is a kind of experimenting, which is cheap, in the same way that "deploying someone else's pre-baked FPGA bitstream, that was designed and synthesized for a $20 target FPGA, onto your own instance of that $20 FPGA, and then feeding your own input signals to it" is cheap. But that's not the kind of experimenting you'd be doing in this course while learning to design your own models!)