GPU Shape

System Requirements

Architecture	Server / HPC	Workstation
Pascal	Tesla P40 Tesla P100	Quadro P5000
Volta	Tesla V100
Turing	Tesla T4	Quadro RTX 5000
Ampere	Tesla A100	RTX A4000 RTX A5000
Ada Lovelace	L4	NVIDIA RTX 4000 SFF Ada NVIDIA RTX 2000 Ada
Hopper	H100

To check the compute capability of Nvidia cards, visit Nvidia's website.

We support only the NVIDIA 'recommended / certified / production branch' Linux drivers for these cards with minimum CUDA version 12.0.
To check Nvidia's driver information, see this page.
To check the compute capability of Nvidia cards, visit Nvidia's website.

We support NVIDIA’s Multi-Instance GPU (MIG) feature. Please make sure that MIG-enabled GPUs are being used in conjunction with a queueing system which supports this feature, see How do I make use of Multi-instance GPUs (MIG).

For information on pre-configured Schrödinger compatible GPU boxes see MD Compatible Systems and FEP+ Compatible Systems.

Standard support does not cover consumer-level GPU cards such as GeForce GTX cards.
In the 2024-2 release, the "Discounted Cores" of an L4 was decreased from 7424 to 5120. This adjustment was made to align with the L4's status as a preferred card due to its widespread availability on-premises and through cloud providers. Additionally, the L4's low power consumption, speed, and sufficient memory make it suitable for running the majority of workflows in a configuration with one GPU per node.
Ensure that your machine has adequate cooling for the graphics cards. Running simulations on a GPU generates a lot of heat which must be dissipated. Inadequate cooling may lead to lower performance and damages of the graphics cards.
The minimum is not necessarily the optimum hardware configuration, particularly if you are planning to run large modeling jobs.
If you already have another NVIDIA GPGPU and would like to know if we have experience with it, please contact our support at help@schrodinger.com.