I thought the problem was that those cards have loads of RAM but lack really important compute capabilities such that they're kind of useless for actually running AI workloads on. Is that not the case?
it is - they're laughably slow and not even supported by latest CUDA
> NVIDIA Driver support for Kepler is removed beginning with R495. CUDA Toolkit development support for Kepler continues through CUDA 11.x.
friend you shouldn't make comments like this unless you understand the definitions of the words. Deepseek wrote some parts of their kernels using PTX. newsflash: PTX support for features is lockstep with CUDA support for the same features ie the fact that CUDA doesn't support it means you couldn't write the PTX to use those features either.
Thank you for providing the information to clear up ignorance though.
> is deepseak's use of PTX instead of CUDA relevant here?
this is a conclusion/assumption thinly veiled as a question
> Deepseek R1 doesn't use CUDA, so ... it isn't a big deal?
note, genuine questions don't already presuppose an answer.
You don't get great out-of-the-box performance but it only took me three work days or so with no experience writing these to adapt, test, and validate a kernel using the acceleration hardware that was available (no prior experience writing these kernels).
They're not as powerful as others but still significantly better than running on a CPU alone and I'd bet my kernel is missing more advanced optimizations.
My issue with these was the power cable and fans. The author touches on the fans and I did try a 3D printed shroud and some of the higher pressure fans but I could only run the cards in short stints. I ended up making an enclosure that went straight out of the case using two high pressure SAN array fans I harvested from the IT graveyard per card and making a hole with an angle grinder.
The power cable is NOT STANDARD on these. I had to find a weird specific cable to adapt the standard 8-pin GPU connector and each card takes two of these bad boys.