Menu
Selecting Instance Sizes
Choose the amount of virtual memory for your workflow. Look for gpu instance types for speeding up deep neural network workflows.
Selecting the right instance size
Small instances 1gb-4gb | Medium instances 8gb-32gb | Large instances 64gb-512gb | GPU instances 16gb+gpu and 61gb+gpu | |
| Resources similar to | smart phone | laptop | workstation | graphics accelerated workstation |
| Jupyter notebook usage | light | heavy | heavy | heavy |
| Good for deep learning (Tensorflow, Keras, Pytorch, images, video, audio) or traditional workloads (csv, parquet, databases, scikit learn, pandas) | traditional | traditional | traditional | deep learning |
| Ability to load at once into memory | small datasets (~1GB-4GB) | large datasets (~4GB-32GB) | large datasets (~32GB-512GB) | large datasets (~16GB-61GB) |
| Capable of batch processing very large datasets with Python | yes | yes | yes | yes |
| Cost to run an hour | $0.02 to $0.08 | $0.16 to $0.64 | $1.28 to $10.24 | $0.50 and $2.00 |
| Tip Running out of memory with your current instance type? Start a larger instance with enough memory to handle your workload – or refactor code to keep memory usage low such as processing large datasets in batches. |