cluster:181
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2019 GPU Models
We do not do AI (yet). The pattern is mostly one job per GPU for exclusive access. So no NVlink requirements, CPI connections sufficient. The application list is Amber, Gromacs, Lammps and some python biosequencing packages. Our current per GPU memory footprint is 8 GB which seems sufficient.
| Quadro | Tesla | Turing | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Model | RTX 2080 Ti | RTX TITAN | RTX 4000 | RTX 6000 | RTX 8000 | P100 | V100 | T4 | Notes |
| Cores | 4352 | 4608 | 2304 | 4608 | 4608 | 3584 | 5120 | 2560 | parallel cuda |
| Memory | 11 | 24 | 8 | 24 | 46 | 12 | 32 | 16 | GB ddr6 |
| Watts | 250 | 280 | 250 | 295 | 295 | 250 | 250 | 70! | |
| Tflops | - | 0.5 | - | 0.5 | - | 4.7 | 7 | - | double fp64 |
| Tflops | 13.5 | 16 | 7 | 16 | 16 | 9.3 | 14 | 8.1 | single fp32 |
| Avg Bench | 197% | 215% | 120% | 207% | 219% | 120% | 150% | ?? | user bench reporting |
| Price | $1,199 | $2,499 | $900 | $4,000 | $5,500 | $4,250 | $9,538 | ?? | list price |
| $/fp32 | $89 | $156 | $129 | $250 | $344 | $457 | $681 | ?? | |
| Notes | small scale | medium scale | small scale | medium scale | large scale | versatile but EOL | most advanced | supercharge | |
| FP64? | - | some | - | some | - | yes | yes | - | double fp64 |
A lot of information comes from this web site Best GPU for deep learning
Bench statistics (Nidia GTX 1070 is about 100% baseline) from this web site External Link
Most GPU models come in multiple memory configurations, showing the most common footprints.
This is a handy tool GPU Server Catalog
Learn more about the T4
cluster/181.1564579313.txt.gz · Last modified: 2019/07/31 09:21 by hmeij07
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