GPU nodes

So you have to connect to our setup to ssh into or or
or or connect via a terminal from within Thinlinc.

Available GPUs

The following NVIDIA GPUs are currently available as part of the DCC managed HPC clusters:

# GPUsNameYearArchitectureCUDA cap.CUDA coresClock MHzMem GiBSP peak GFlopsDP peak GFlopsPeak GB/s
5Tesla K40c2013GK110B (Kepler)3.52880745 / 87511.174291 / 50401430 / 1680288
8Tesla K80c (dual)2014GK210 (Kepler)3.72496562 / 87511.172796 / 4368932 / 1456240
8*TITAN X2016GP102 (Pascal)6.135841417 / 153111.9010157 / 10974317.4 / 342.9480
22Tesla V1002017GV100 (Volta)7.05120138015.75141317065898
12Tesla V100-SXM22018GV100 (Volta)7.05120153031.72156677833898
6Tesla A100-PCIE2020GA100 (Ampere)8.06912141039.591949297461555
Tesla H100-PCIE
2022GH100 (Hopper)9.07296175579.1851200256002048
-Tesla H100-SXM52022GH100 (Hopper)9.08448198079.1866900335003352

*Please note that the NVIDIA consumer GPUs TITAN X do not support ECC (error correction code).

Running interactively on GPUs

At the moment, there are currently three kind of nodes available for running interactive jobs on NVIDIA GPUs: Tesla V100 and Tesla V100-SXM2 both based on the Volta architecture and Tesla A100 with the Ampere Architecture. To run interactively on on a Tesla V100 node, you can use the command


This node has 2 Nvidia-Volta-100 GPUs, each with 16GB of memory.
To run interactively on on a Tesla V100-SXM2 node, you can use the command


This node has 4 Nvidia-Volta-100 GPUs, each with 32GB of memory.

This node has 2 A100-GPUs, each with 40GB of memory. You can get an interactive shell there with


Please note that multiple users are allowed on these nodes, and all users will be able to access all the GPUs on the node. We have set the GPUs to the “Exclusive process” runtime mode, which means that you will encounter a “device not available” (or similar) error, if someone is using the GPU you are trying to access.

In order to avoid too many conflicts we ask you to follow this code-of-conduct:

  • Please monitor which GPUs are currently occupied using the command nvidia-smi and predominantly select unoccupied GPUs (e.g., using cudaSetDevice()) for your application.
  • If you need to run on all CPU cores, e.g., for performance profiling, please make sure that you are not disturbing other users.
  • We kindly ask you to use the interactive nodes mainly for development, profiling, and short test jobs.
  • Please submit ‘heavy’ jobs into the gpu-queue and don’t use the interactive nodes for heavy stuff

If you have further questions or issues using the GPUs please write to

Requesting GPUs under LSF10 for non-interactive use

For submitting jobs into the LSF10-setup, please follow these instructions:
Using GPUs under LSF10

If you have further questions or issues using the GPUs please write to