Choosing to “Run in Studio” versus “Run as Job”
Python models can be run on your “local” IDE instance or you can leverage the power of hyper-scaling by running the module as a Job. When running as a job you have access to a number of different machine configurations as follows:
For reference, a typical laptop will be equivalent to either a XS or S machine configuration.
| Name | CPU | RAM | Run Rate |
| Mini | 0.5v Core | 500MB | 0.5 |
| 4XS | 1v Core | 1GB | 1 |
| 3XS | 1v Core | 2GB | 2 |
| 2XS | 2v Core | 4GB | 3 |
| XS | 4v Core | 8GB | 4 |
| S | 4v Core | 16GB | 5 |
| M | 6v Core | 16GB | 6 |
| L | 6v Core | 32GB | 7 |
| XL | 8v Core | 64GB | 8 |
| 2XL | 8v Core | 128GB | 9 |
| 3XL | 10v Core | 192GB | 10 |
| 4XL | 10v Core | 256GB | 11 |
Complex optimization models may require more CPU cores to solve quickly and large scale simulations may require the use of more RAM due to increase in data required for model fidelity.