Einsteinium GPU Cluster

Einsteinium is an institutional GPU cluster that was deployed to meet the growing computational demand for researchers doing machine learning and deep learning. The system is named after the chemical element with symbol Es and atomic number 99 which was discovered at Lawrence Berkeley National Laboratory in 1952 and in honor of Albert Einstein who developed the theory of relativity.

es2 Partition

es2 is a partition consisting of H100 and H200 GPU nodes. These nodes have been added in 2024 and 2025 to meet AI/LLM related research needs in addition to growing usage of GPUs in scientific computing.

Accelerator	Nodes	GPUs per Node/GPU Memory	CPU Processor	CPU Cores	CPU RAM	Infiniband
NVIDIA H200	3	8x 141 GB	Intel Xeon Platinum 8570	112	2 TB	NDR
NVIDIA H100	4	8x 80 GB	Intel Xeon Platinum 8480+	112	1 TB	NDR

How to specify desired GPU card(s)

The normal qos for the es2 partition is called es2_normal. The other qos values are es_debug and es_lowprio.

Due to hardware configuation, special attention is needed to ensure the ratio of CPU-core# to GPU#

Examples:

• Request three H100 cards: --partition=es2 --gres=gpu:H100:3 --cpus-per-task=14 --ntasks=3
• Request one H200 cards: --partition=es2 --gres=gpu:H200:1 --cpus-per-task=14 --ntasks=1

Example slurm script on es2

Here is an example slurm script that requests three NVIDIA H100 GPU cards.

Three GPUs on a H100 nodeSingle GPU on a H200 node

#!/bin/bash
#SBATCH --job-name=test
#SBATCH --account=account_name
#SBATCH --partition=es2
#SBATCH --qos=es2_normal
#SBATCH --nodes=1
#SBATCH --ntasks=3
#SBATCH --cpus-per-task=14
#SBATCH --gres=gpu:H100:3
#SBATCH --time=1:00:00

module load ml/pytorch
python train.py

#!/bin/bash
#SBATCH --job-name=test
#SBATCH --account=account_name
#SBATCH --partition=es2
#SBATCH --qos=es2_normal
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=14
#SBATCH --gres=gpu:H200:1
#SBATCH --time=1:00:00

module load ml/pytorch
python train.py

es1 Partition

es1 is a partition consisting of multiple GPU node types to address the different research needs. These include:

Accelerator	Nodes	GPUs per Node/GPU Memory	CPU Processor	CPU Cores	CPU RAM	Infiniband
NVIDIA A100	1	4x 80 GB	AMD EPYC 7713	64	512 GB	HDR
NVIDIA A40	30	4x 48 GB	AMD EPYC 7742	64	512 GB	FDR
NVIDIA GRTX8000	1	4x 48 GB	AMD EPYC 7713	64	512 GB	HDR
NVIDIA V100	15	2x 32 GB	Intel Xeon E5-2623	8	64GB or 192GB	FDR

How to specify desired GPU card(s)

The normal qos for the es1 partition is called es_normal.

Due to hardware configuation, special attention is needed to ensure the ratio of CPU-core# to GPU#

Examples:

• Request one V100 card: --cpus-per-task=4 --gres=gpu:V100:1 --ntasks=1
• Request two A40 cards: --cpus-per-task=16 --gres=gpu:A40:2 --ntasks=2
• Request one A100 cards: --cpus-per-task=16 --gres=gpu:A100:1 --ntasks=1
• Request four GRTX8000 cards: --cpus-per-task=16 --gres==gpu:GRTX8000:4 --ntasks=4

Example slurm script on es1

Here is an example slurm script that requests one NVIDIA A40 GPU card.

Single GPU on a A40 node2 GPUs on a V100 node

#!/bin/bash
#SBATCH --job-name=test
#SBATCH --account=account_name
#SBATCH --partition=es1
#SBATCH --qos=es_normal
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=16
#SBATCH --gres=gpu:A40:1
#SBATCH --time=1:00:00

module load ml/pytorch
python train.py

#!/bin/bash
#SBATCH --job-name=test
#SBATCH --account=account_name
#SBATCH --partition=es1
#SBATCH --qos=es_normal
#SBATCH --nodes=1
#SBATCH --ntasks=2
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:V100:2
#SBATCH --time=1:00:00

module load ml/pytorch
python train.py

es0 Partition

es0 is a partition with NVIDIA 2080 TI GPUs that do not incur Service Unit (SU) charges.

Accelerator	Nodes	GPUs per Node/GPU Memory	CPU Processor	CPU Cores	CPU RAM	Infiniband
NVIDIA 2080TI	12	4x 11 GB	Intel Xeon Silver 4212	8	96GB	FDR

Example slurm script on es0

Single GPUFour GPUs on a node

#!/bin/bash
#SBATCH --job-name=testes0
#SBATCH --account=account_name
#SBATCH --partition=es0
#SBATCH --qos=es_normal
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
#SBATCH --gres=gpu:1
#SBATCH --time=1:00:00

module load ml/pytorch
python train.py

#!/bin/bash
#SBATCH --job-name=testes0
#SBATCH --account=account_name
#SBATCH --partition=es0
#SBATCH --qos=es_normal
#SBATCH --nodes=1
#SBATCH --ntasks=4
#SBATCH --cpus-per-task=2
#SBATCH --gres=gpu:4
#SBATCH --time=1:00:00

module load ml/pytorch
python train.py