some AI coprocessors, you’re right. they can’t be used.
Most of what nVidia is offering, while lacking display outputs, can still be used for gaming/VDI/vGPU use, and in general aren’t particularly bad at it, usually quite the opposite. might take a bit of work to get them going, and nVidia hasn’t made it easy with their whole licensing thing. Putting aside the challenges, it’s usually possible to adapt their SXM datacenter GPU modules to work with more common frameworks like PCIe, and then utilize those as virtual GPUs in a virtual desktop. This is not dissimilar to what they’re doing with the geforce now platform.
The big benefit of the chips is that they come with loads of memory, so cutting them up as a vGPU is actually a really optimal use-case. Most games are memory limited, which is to say they need more memory than most GPUs have, so the limit in performance is from the memory limitations, not the graphics processing limitations.
The AI processors that are GPU based, like almost everything that nvidia is using, can mostly be repurposed, but those that are ML-only, like AI coprocessors that show up in CPUs like the AI optimized CPUs from AMD and Intel, can’t really be re-used, but they’re usually not released as a standalone product. Those may still be able to perform physics processing offloads… so maybe not useless? I’m not sure.
some AI coprocessors, you’re right. they can’t be used.
Most of what nVidia is offering, while lacking display outputs, can still be used for gaming/VDI/vGPU use, and in general aren’t particularly bad at it, usually quite the opposite. might take a bit of work to get them going, and nVidia hasn’t made it easy with their whole licensing thing. Putting aside the challenges, it’s usually possible to adapt their SXM datacenter GPU modules to work with more common frameworks like PCIe, and then utilize those as virtual GPUs in a virtual desktop. This is not dissimilar to what they’re doing with the geforce now platform.
The big benefit of the chips is that they come with loads of memory, so cutting them up as a vGPU is actually a really optimal use-case. Most games are memory limited, which is to say they need more memory than most GPUs have, so the limit in performance is from the memory limitations, not the graphics processing limitations.
The AI processors that are GPU based, like almost everything that nvidia is using, can mostly be repurposed, but those that are ML-only, like AI coprocessors that show up in CPUs like the AI optimized CPUs from AMD and Intel, can’t really be re-used, but they’re usually not released as a standalone product. Those may still be able to perform physics processing offloads… so maybe not useless? I’m not sure.