Row 80348

Row ID: 80348 | Dataset Entry | Axioma AXP Content Repository

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I dont know if someone has done benchmarking on these GPUs for a certain model. I would have to search the internet which is something that you could also do. However, I would invest in the largest possible amount of GPU memory. If you can only fit a small model to your GPU, I dont think the FLOPS/TOPS (floating point operations per second/trillios operations per second) matter that much since small models are faster to train anyways. Compare the FLOPS/TOPS attributes between the devices that have the largest amount of memory.

FieldValue
text I dont know if someone has done benchmarking on these GPUs for a certain model. I would have to search the internet which is something that you could also do. However, I would invest in the largest possible amount of GPU memory. If you can only fit a small model to your GPU, I dont think the FLOPS/TOPS (floating point operations per second/trillios operations per second) matter that much since small models are faster to train anyways. Compare the FLOPS/TOPS attributes between the devices that ha…
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-24
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{
  "text": "I dont know if someone has done benchmarking on these GPUs for a certain model. I would have to search the internet which is something that you could also do. However, I would invest in the largest possible amount of GPU memory. If you can only fit a small model to your GPU, I dont think the FLOPS/TOPS (floating point operations per second/trillios operations per second) matter that much since small models are faster to train anyways. Compare the FLOPS/TOPS attributes between the devices that have the largest amount of memory.",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-24",
  "username_encoded": "Z0FBQUFBQm5Lak1scXpfZThKbE43RTZJZGRBU1I5QldOXzFUc1Q3RzFjSmxSeF9GME9ZcWlHd3gzZU9OYjQ4SWhOajBreTlLQmdVWU8wSFpNMlY2X1QtTWxqSVNBRG0tUkE9PQ==",
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}
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