Choosing the Best GPU for Deep Learning in 2020

State-of-the-art (SOTA) deep learning models have massive memory footprints. Many GPUs don't have enough VRAM to train them. In this post, we determine which GPUs can train state-of-the-art networks without throwing memory errors. We also benchmark each GPU's training performance.

TLDR

The following GPUs can train all SOTA language and image models as of February 2020:

• RTX 8000: 48 GB VRAM, ~$5,500.
• RTX 6000: 24 GB VRAM, ~$4,000.
• Titan RTX: 24 GB VRAM, ~$2,500.

The following GPUs can train most (but not all) SOTA models:

• RTX 2080 Ti: 11 GB VRAM, ~$1,150. *
• GTX 1080 Ti: 11 GB VRAM, ~$800 refurbished. *
• RTX 2080: 8 GB VRAM, ~$720. *
• RTX 2070: 8 GB VRAM, ~$500. *

The following GPU is not a good fit for training SOTA models:

• RTX 2060: 6 GB VRAM, ~$359.

* Training on these GPUs requires small batch sizes, so expect lower model accuracy because the approximation of a model's energy landscape will be compromised.

Image models

Maximum batch size before running out of memory

Performance, measured in images processed per second

Language models

Maximum batch size before running out of memory

Performance

Results normalized by Quadro RTX 8000

Conclusions

• Language models benefit more from larger GPU memory than image models. Note how the right diagram is steeper than the left. This indicates that language models are more memory-bound and image models are more computationally bounded.
• GPUs with higher VRAM have better performance because using larger batch sizes helps saturate the CUDA cores.
• GPUs with higher VRAM enable proportionally larger batch sizes. Back-of-the-envelope calculations yield reasonable results: GPUs with 24 GB of VRAM can fit a ~3x larger batches than a GPUs with 8 GB of VRAM.
• Language models are disproportionately memory intensive for long sequences because attention is quadratic to the sequence length.

GPU Recommendations

• RTX 2060 (6 GB): if you want to explore deep learning in your spare time.
• RTX 2070 or 2080 (8 GB): if you are serious about deep learning, but your GPU budget is $600-800. Eight GB of VRAM can fit the majority of models.
• RTX 2080 Ti (11 GB): if you are serious about deep learning and your GPU budget is ~$1,200. The RTX 2080 Ti is ~40% faster than the RTX 2080.
• Titan RTX and Quadro RTX 6000 (24 GB): if you are working on SOTA models extensively, but don't have budget for the future-proofing available with the RTX 8000.
• Quadro RTX 8000 (48 GB): you are investing in the future and might even be lucky enough to research SOTA deep learning in 2020.

Lambda offers GPU laptops and workstations with GPU configurations ranging from a single RTX 2070 up to 4 Quadro RTX 8000s. Additionally, we offer servers supporting up to 10 Quadro RTX 8000s or 16 Tesla V100 GPUs.

Footnotes

Image Models

Language Models