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GKozinski
GKozinski
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All cards from this series support CUDA. In fact they even have special cores, designed for faster deep learning calculations called 'tensorcores'.

If you want to do some deep learning with big models (NLP, computer vision, GAN) you should also focus on amount of VRAM to fit such models. Nowadays I would say at least 12GB should suffice for some time.

So I would select cards with minimum 12GB and buy the best you can afford.

Personally, I would probably focus on 3090 and not 3090 ti, as the price increase is pretty significant and probably not worth the increase in computational power.

Also if you're new to ML/DL, probably you should first learn some of this stuff before deciding on spending money on equipement. Not all ML/DL models benefits from using GPU over CPU. Smaller/simpler models are trained faster on CPU. More on that matter: Is a GPU always faster than a CPU for training neural networks?

All cards from this series support CUDA. In fact they even have special cores, designed for faster deep learning calculations called 'tensorcores'.

If you want to do some deep learning with big models (NLP, computer vision, GAN) you should also focus on amount of VRAM to fit such models. Nowadays I would say at least 12GB should suffice for some time.

So I would select cards with minimum 12GB and buy the best you can afford.

Personally, I would probably focus on 3090 and not 3090 ti, as the price increase is pretty significant and probably not worth the increase in computational power.

Also if you're new to ML/DL, probably you should first learn some of this stuff before deciding on spending money on equipement. Not all ML/DL models benefits from using GPU over CPU. Smaller/simpler models are trained faster on CPU.

All cards from this series support CUDA. In fact they even have special cores, designed for faster deep learning calculations called 'tensorcores'.

If you want to do some deep learning with big models (NLP, computer vision, GAN) you should also focus on amount of VRAM to fit such models. Nowadays I would say at least 12GB should suffice for some time.

So I would select cards with minimum 12GB and buy the best you can afford.

Personally, I would probably focus on 3090 and not 3090 ti, as the price increase is pretty significant and probably not worth the increase in computational power.

Also if you're new to ML/DL, probably you should first learn some of this stuff before deciding on spending money on equipement. Not all ML/DL models benefits from using GPU over CPU. Smaller/simpler models are trained faster on CPU. More on that matter: Is a GPU always faster than a CPU for training neural networks?

Source Link
GKozinski
GKozinski
  • 1.3k
  • 10
  • 20

All cards from this series support CUDA. In fact they even have special cores, designed for faster deep learning calculations called 'tensorcores'.

If you want to do some deep learning with big models (NLP, computer vision, GAN) you should also focus on amount of VRAM to fit such models. Nowadays I would say at least 12GB should suffice for some time.

So I would select cards with minimum 12GB and buy the best you can afford.

Personally, I would probably focus on 3090 and not 3090 ti, as the price increase is pretty significant and probably not worth the increase in computational power.

Also if you're new to ML/DL, probably you should first learn some of this stuff before deciding on spending money on equipement. Not all ML/DL models benefits from using GPU over CPU. Smaller/simpler models are trained faster on CPU.