Submitted by et_tu_brutits t3_xt7vcn in MachineLearning
Friends,
Would appreciate some insight/guidance in choosing the optimal GPU for general training purposes against some constraints I won't delve into at much detail.
I run a bare metal hypervisor on a Dell R820 and plan to perform GPU passthrough and have some constraints which restrict me to either a 3060 RTX or 2060 RTX. Cost isn't an issue
Card | Memory | Tensor Cores | Cuda Cores | Core | Boost |
---|---|---|---|---|---|
2060 RTX | 12GB | 240 | 1920 | 1365mhz | 1680mhz |
3060 RTX | 12GB | 112 | 3584 | 1320mhz | 1780mhz |
Considerations:
-
2060 has more tensor cores, however 3060 Ampere represents 50% faster per tensor core operations than Turing. For tensor cores, including clock speeds, I think the 2060 slightly has the edge or might be equivalent?
-
The 3060 clearly wins with CUDA cores
I'm likely turd polishing, however I am leaning towards the 3060 on account of longer term support for libraries. I also don't have experience with either card, so don't know if the additional 3060 CUDA cores will make a major difference in Tensorflow/PyTorch.
What's your recommendation to maximize value and future reuse for general purpose training? Thank you in advance and have a splendid weekend.
suflaj t1_iqou52g wrote
The tensor and cuda cores between these 2 are not comparable. I don't know what support for libraries mean, CUDA capability versions are rarely relevant for DL and the cards are not very relevant now, let alone 10 years from now when something for their generation might start to get deprecated. You must realize that even if you bought a 4090 on this very day, a product that is soon only coming out, it is going to be obsolete in 2-4 years.
The 3060 is comparable to the 2080. The 2060 is not even comparable to any last gen cards. Obviously the answer is 3060.