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Why do core counts and memory type matter when the table includes memory bandwith and tflop16?
The H200 has HBM and alot of tensor cores which is reflected in its high stats in the table and the amd gpus don't have cuda cores.
I know a major deterioration is to be expected but how major? Even in extreme cases with only 10% efficiency of the total power then its still competitive against the H200 since you can get way more for the price, even if you can only use 10% of that.
Tflops is a generic measurement, not actual utilization, and not specific to a given type of workload. Not all workloads saturate gpu utilization equally and ai models will depend on cuda/tensor. the gen/count of your cores will be better optimized for AI workloads and better able to utilize those tflops for your task. and yes, amd uses rocm which i didn't feel i needed to specify since its a given (and years behind cuda capabilities). The point is that these things are not equal and there are major differences here alone.
I mentioned memory type since the cards you listed use different versions ( hbm vs gddr) so you can't just compare the capacity alone and expect equal performance.
And again for your specific use case of this large MoE model you'd need to solve the gpu-to-gpu communication issue (ensuring both connections + sufficient speed without getting bottlenecked)
I think you're going to need to do actual analysis of the specific set up youre proposing. Good luck