
A client involved in cardiovascular research approached us with a request to introduce a machine for medical image deep learning with a budget of less than 300 million yen.
The intended use is machine learning of clinical data using electrocardiogram and phonocardiogram waveforms, pathology images, etc., and the client would like to have a parts configuration with as high performance as possible within the budget.
Based on the information we received from our customers, we proposed the following configuration:
CPU | Intel Xeon W7-2595X 2.80GHz (up to 3.0GHz at TB4.8) 26C/52T |
memory | Total 256GB DDR5 5600 REG ECC 64GB x 4 |
storage | 2TB SSD M.2 NVMe Gen4 |
Video | NVIDIA RTX6000 Ada 48GB (DisplayPort x4) |
network | on board (1GbE x1 /10GbE x1) |
Housing + power supply | Mid tower case + 1600W 80PLUS TITANIUM |
OS | Microsoft Windows 11 Professional 64bit |
Other | TPM module |
The CPU is a "Xeon W7-2595X 26-core" and it is equipped with 256GB of memory (64GB x 4).
For Deep Learning, we chose the RTX6000 Ada 48GB GPU, a high-end model for workstations.
The power supply unit is a 1600W model, with plenty of room to add a GPU in the future.
If you don't plan on adding a GPU, you can keep the price down by changing the power supply capacity.
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