This is an old revision of the document!
2019 GPU Models
We do not do AI (yet). The GPU usage 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 |
| Price | $1,199 | $2,499 | $900 | $4,000 | $5,500 | $4,250 | $9,538 | $2,200 | list price |
| $/fp32 | $89 | $156 | $129 | $250 | $344 | $457 | $681 | $272 | |
| 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 … the T4 can run in mixed mode (fp32/fp16) and deliver 65 Tflops. Other modes are INT8 at 130 Tops and INT4 260 Tops. Now at 65 Tflops mixed precision the cost dives to $34/tflop. Amazing. And the wattage is amazing too.
Keep track of this; does Amber run on the T4, the web site lists “Turing (SM_75) based cards require CUDA 9.2 or later.” but does not list the T4 (too new?).
