GPU Resources on Tufts HPC Cluster

GPUs

NVIDIA GPUs are available in gpu and preempt partitions

• Request GPU resources with --gres. See details below.

• If no specific architecture is required, GPU resources can be request with--gres=gpu:1 (one GPU)

• Please DO NOT manually set CUDA_VISIBLE_DEVICES.

• Users can ONLY see GPU devices that are assigned to them with $ nvidia-smi.

• gpu partition-p gpu:

  • NVIDIA A100

    • In “gpu” partition

    • Request with: --gres=gpu:a100:1(one A100 GPU, can request up to 8 on one node)

    • Each GPU comes with 80GB of DRAM

  • NVIDIA P100s

    • In “gpu” partition

    • Request with: --gres=gpu:p100:1(one P100 GPU, can request up to 6 on one node)

    • Each GPU comes with 16GB of DRAM

  • NVIDIA Tesla K20xm

    • In “gpu” partition

    • Request with: --gres=gpu:k20xm:1(one Tesla K20xm GPU, can request up to 1 on one node)

    • Each GPU comes with 6GB of DRAM

• preempt partition -p preempt:

  • a100, v100, p100, rtx_6000, rtx_a6000,rtx_6000ada, t4

  • NVIDIA T4

    • In “preempt” partition

    • Request with: --gres=gpu:t4:1(one T4 GPU, can request up to 4 on one node)

    • Each GPU comes with 16GB of DRAM

  • NVIDIA P100

    • In “preempt” partition

    • Request with: --gres=gpu:p100:1(one P100 GPU, can request up to 4 on one node)

    • Each GPU comes with 16GB of DRAM

  • NVIDIA rtx_6000

    • In “preempt” partition

    • Request with: --gres=gpu:rtx_6000:1(one RTX_6000 GPU, can request up to 8 on one node)

    • Each GPU comes with 24GB of DRAM

  • NVIDIA rtx_a6000

    • In “preempt” partition

    • Request with: --gres=gpu:rtx_a6000:1(one RTX_A6000 GPU, can request up to 8 on one node)

    • Each GPU comes with 48GB of DRAM

  • NVIDIA rtx_6000ada

    • In “preempt” partition

    • Request with: --gres=gpu:rtx_6000ada:1(one RTX_6000Ada GPU, can request up to 4 on one node)

    • Each GPU comes with 48GB of DRAM

  • NVIDIA V100

    • In “preempt” partition

    • Request with: --gres=gpu:v100:1(one V100 GPU, can request up to 4 on one node)

    • Each GPU comes with 16GB of DRAM

  • NVIDIA A100

    • In “preempt” partition

    • Request with: --gres=gpu:a100:1(one A100 GPU, can request up to 8 on one node)

    • Each GPU comes with 40GB of DRAM or 80GB of DRAM