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From https://www.nvidia.com/en-us/deep-learning-ai/products/titan-rtx/ https://www.nvidia.com/en-us/deep-learning-ai/products/titan...: "NVIDIA TITAN RTX NVLin
by paol 6y ago
From https://www.nvidia.com/en-us/deep-learning-ai/products/titan-rtx/ https://www.nvidia.com/en-us/deep-learning-ai/products/titan...:
"NVIDIA TITAN RTX NVLink Bridge
The TITAN RTX NVLink™ bridge connects two TITAN RTX cards together over a 100 GB/s interface. The result is an effective doubling of memory capacity to 48 GB, so that you can train neural networks faster, process even larger datasets, and work with some of the biggest rendering models."
- deleted 6y ago[deleted]
- colincooke 6y agoYeah you're not wrong, but it's a bit misleading. This allows you to run faster, but it does it by allowing you to use a larger batch size (arguably not best practice but your mileage will vary). Memory pooling is a bit different in that you can treat the combined cards as a single card from TF/pytorch.
- ivalm 6y agoBut batch size is prob least problem since you can do data parallelism (send half batch to each gpu, combine on cpu). I think only model bigger than gpu mem is where you really wish for nvlink on v100s.