Post by account_disabled on Mar 14, 2024 15:01:41 GMT 5.5
For clarity we have converted the table values into graphs. Regardless of the size of the model being trained and the number of A tokens Ada is predictably faster. As we have already noted training of the largest model metallamaLlamabchathf on the A was possible only when the LoRA configuration was changed. But even so it was not completed. However this in no way means that the A is unsuitable for generating text with large language models. During testing we did not consider all options. For example they did not combine two As via NVLink.
Conclusion The obvious conclusion is that the A Ada has better performance than the A. For Buy Email List resourceintensive tasks use it. But for the sake of such a conclusion it was not worth conducting the test. The use of the A is justified in training light and medium LLMs when shipping small batches this video card copes with tasks in the same time as the A Ada or even faster. The A Ada due to its larger memory capacity and number of cores leaves room for experimentation with batch sizes. The A Ada copes faster with generative tasks for which everything is started so this card looks interesting for working with already trained.
NVLink imposes restrictions on the operation of the A Ada this is excellent hardware for tasks to which she will devote herself entirely and which she will decide alone. If sharing resources or combining cards to increase computing power is relevant it is worth considering the A. As you can see we do not have a clear conclusion about which card is better for working with LLM. We conducted testing to see the real capabilities of the A in comparison with the A Ada under the same conditions. The choice of a particular GPU model should be made based on the number and complexity of real tasks resource sharing capabilities.
Conclusion The obvious conclusion is that the A Ada has better performance than the A. For Buy Email List resourceintensive tasks use it. But for the sake of such a conclusion it was not worth conducting the test. The use of the A is justified in training light and medium LLMs when shipping small batches this video card copes with tasks in the same time as the A Ada or even faster. The A Ada due to its larger memory capacity and number of cores leaves room for experimentation with batch sizes. The A Ada copes faster with generative tasks for which everything is started so this card looks interesting for working with already trained.
NVLink imposes restrictions on the operation of the A Ada this is excellent hardware for tasks to which she will devote herself entirely and which she will decide alone. If sharing resources or combining cards to increase computing power is relevant it is worth considering the A. As you can see we do not have a clear conclusion about which card is better for working with LLM. We conducted testing to see the real capabilities of the A in comparison with the A Ada under the same conditions. The choice of a particular GPU model should be made based on the number and complexity of real tasks resource sharing capabilities.