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Accelerator cards

MCFC765
Go to solution Solved by da na,

Accelerator cards are for use in servers and datacenters. You could use them for CAD and Blender but I would seriously recommend against doing so. They are meant to accelerate either x86 compute performance or perform data processing tasks based on which accelerator you choose (x86 accel would be Xeon Phi, Nvidia Tesla would be data processing)

You could buy a Tesla for CAD but again I would recommend just 2 gpus in SLI. You need a specific model Quadro for the Tesla to work properly and they aren't compatible with many applications.

I've seen so many cards that are called accelerator cards on eBay. But what are accelerator cards, what do they do and can I use them for stuff like CAD or Blender? 

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Accelerator cards are for use in servers and datacenters. You could use them for CAD and Blender but I would seriously recommend against doing so. They are meant to accelerate either x86 compute performance or perform data processing tasks based on which accelerator you choose (x86 accel would be Xeon Phi, Nvidia Tesla would be data processing)

You could buy a Tesla for CAD but again I would recommend just 2 gpus in SLI. You need a specific model Quadro for the Tesla to work properly and they aren't compatible with many applications.

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2 minutes ago, Mel0nMan said:

Accelerator cards are for use in servers and datacenters. You could use them for CAD and Blender but I would seriously recommend against doing so. They are meant to accelerate either x86 compute performance or perform data processing tasks based on which accelerator you choose (x86 accel would be Xeon Phi, Nvidia Tesla would be data processing)

You could buy a Tesla for CAD but again I would recommend just 2 gpus in SLI. You need a specific model Quadro for the Tesla to work properly and they aren't compatible with many applications.

Thanks man!

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"accelerator card" is just a generic term for anything that plugs onto your PCIe slot (usually) and accelerates some tasks.

 

As mentioned before, you could use Xeon Phis (bunch of low performance x86 cores) for manycore tasks, or a tesla (basically a GPU with no video outputs) for ML, or even a FPGA tailored for your specific workload.

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