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The table is based on July 26, 2023 data.

Slurm Partition name

Requestable features

Node
count

Sockets/
Node

Cores/
Socket

Threads/
Core

Total
Cores/
Node

RAM
(GB)

Processor (x86_64)

Local Disks

OS

Use Case

Key Attributes

moran

fdr, intel, sandy, ivy, community

273

2

8

1

16

64 or 128

Intel Ivybridge/
Sandybridge

1 TB HD

RHEL 8.8

For compute jobs not needing the latest and greatest hardware.

Original Moran compute

moran-bigmem

fdr, intel, haswell

2

2

8

1

16

512

Intel Haswell

1 TB HD

RHEL 8.8

For jobs not needing the latest hardware, w/ above average memory requirements.

Moran compute w/ 512G of RAM

moran-hugemem

fdr, intel, haswell, community

2

2

8

1

16

1024

Intel Haswell

1 TB HD

RHEL 8.8

For jobs that don’t need the latest hardware, w/ escalated memory requirements.

Moran compute w/ 1TB of RAM

dgx

edr, intel, broadwell

2

2

20

2

40

512

Intel Broadwell

7 TB SSD

RHEL 8.8

For GPU and AI-enabled workloads.

Special DGX GPU compute nodes

teton

edr, intel, broadwell, community

175

2

16

1

32

128

Intel Broadwell

240 GB SSD

RHEL 8.8

For regular compute jobs.

Teton compute

teton-cascade

edr, intel, cascade, community

56

2

20

1

40

192 or 768

Intel Cascade Lake

240 GB SSD

RHEL 8.8

For compute jobs w/ on newer-older hardware, and somewhat higher memory requirements.

Teton compute w/ Cascade Lake CPUs

teton-gpu

edr, intel, broadwell, community

6

2

16

1

32

512

Intel Broadwell

240 GB SSD

RHEL 8.8

For compute jobs utilizing GPUs on prior cluster hardware.

Teton GPU compute

teton-hugemem

edr, intel, broadwell

8

2

16

1

32

1024

Intel Broadwell

240 GB SSD

RHEL 8.8

For compute jobs w/ large memory requirements, running on fast prior cluster hardware.

Teton compute w/ 1TB of RAM

teton-massmem

edr, amd, epyc

2

2

24

1

48

4096

AMD/EPYC

4096 GB SSD

RHEL 8.6

For compute jobs w/ exceedingly demanding memory requirements

Teton compute w/ 4TB of RAM

teton-knl

edr, intel, knl

12

1

72

4

72

384

Intel Knights Landing

240 GB SSD

RHEL 8.8

For jobs using many cores on a single node, but speed isn’t critical

Teton compute w/ Intel Knight’s Landing CPU’s

beartooth

edr, intel, icelake

2

2

28

1

56

256

Intel Icelake

436 GB SSD

RHEL 8.8

For general compute jobs running the latest and greatest Beartooth hardware

Beartooth compute

beartooth-gpu

edr, intel, icelake

4

2

28

1

56

250 or 1024

Intel Icelake

436 GB SSD

RHEL 8.8

For compute jobs needing GPU on the latest and greatest hardware.

Beartooth GPU compute

beartooth-bigmem

edr, intel, icelake

6

2

28

1

56

515

Intel Icelake

436 GB SSD

RHEL 8.8

For jobs w/ above average memory requirements, on the latest and greatest hardware.

Beartooth compute w/ 512G of RAM

beartooth-hugemem

edr, intel, icelake

8

2

28

1

56

1024

Intel Icelake

436 GB SSD

RHEL 8.8

For jobs w/ large memory requirements on the latest and greatest hardware.

Beartooth compute w/ 1TB of RAM

medicinebow

amd, epyc

25

2

48

1

96

1024

AMD EPYC

4TB SSD

RHEL 8.8

You tell me, Homies.

You tell me, Homes.

medicinebow-a30

amd, epyc

8

2

48

1

96

768

AMD EPYC

4TB SSD

RHEL 8.8

You tell me, Homies.

You tell me, Homies.

medicinebow-l40s

amd, epyc

5

2

48

1

96

768

AMD EPYC

4TB SSD

RHEL 8.8

You tell me, Homies.

You tell me, Homies.

medicinebow-h100

amd, epyc

6

2

48

1

96

768

AMD EPYC

4TB SSD

RHEL 8.8

You tell me, Homies.

You tell me, Homies.

...

GPU Type

Partition

Example slurm value to request

# of Nodes

GPU devices per node

CUDA Cores

Tensor Cores

GPU Memory Size (GB)

Compute Capability

Tesla P100

teton-gpu

(all available on non-investor)

Code Block
#SBATCH --partition=teton-gpu
#SBATCH --gres=gpu:?

8

2

3584

0

16

6.0

V100

dgx

(both available on non-investor)

Code Block
#SBATCH --partition=dgx
#SBATCH --gres=gpu:?

2

8

5120

640

16/32

7.0

A30

beartooth-gpu (4)

medicinebow-gpu? (8)

non-investor (3)

Code Block
#SBATCH --partition=beartooth-gpu
#SBATCH --gres=gpu:?

15

2 7 on BT/non-investor, 8 on MedicineBow

3584

224

25

8.0

T4

non-investor

2

3

2560 or
3804 FP32 CUDA/GPU on MB

320 or 224 TC/GPU on MB

16G
24GB/GPU on MB

7.5

L40S

medicinebow-gpu? (5)

5

8

568 TC/GPU on MB

48GB/GPU

H100

medicinebow-gpu? (6)

6

8

16896 FP32 CUDA/GPU 

528 TC/GPU on MB

80GB/GPU

...