NEMIX RAM DDR5-5600 2TB (16x128GB) ECC RDIMM CL46 1.1V Supermicro H14SST-G memory upgrade
- 2TB (16 x 128GB)
- 288-Pin DDR5 RDIMM
Me võtame vastu krüptoraha — nii saab teie hoitav krüptoraha osta päris riistvara.Makske krüptorahaga, kulutage seda päris riistvarale. Tasuta kindlustatud tarne kogu maailmas, üle 399 $. Lihtsad, kaubamärgita väliskarbid, saadetud Saksamaalt.Tasuta kindlustatud tarne üle 399 $. Teie kurss lukustatakse 30 minutiks kohe, kui kassa avaneb.Kurss lukustatud 30 minutiks. Iga toode on pitseeritud, selle seerianumber on kontrollitud ja sellel kehtib 24-kuuline garantii.Pitseeritud, 24-kuulise garantiiga.
Lenovo
A 32 GB HBM2 passive plug-in card for compute workloads using OpenACC, OpenCL and DirectCompute
The Lenovo V100 is a 32 GB HBM2 workstation graphics card built on the NVIDIA V100 chipset. It uses a passive plug-in card form factor and supports OpenACC, OpenCL and DirectCompute APIs for compute workloads.
This card suits developers and researchers running GPU-accelerated simulations, deep-learning training or high-performance analytics that benefit from high-bandwidth memory. It is not for buyers who need active cooling, display outputs or gaming-oriented driver features.
The 32 GB HBM2 frame buffer is the specification that determines whether this GPU can hold your model, dataset or simulation in memory. If your workload fits, the bandwidth and capacity match; if it exceeds this limit, you must consider a different configuration.
| Brand | Lenovo |
|---|---|
| Chipset Manufacturer | NVIDIA |
| GPU | V100 |
| Memory Type | HBM2 |
| DirectX | OpenACC OpenCL DirectCompute |
| OpenGL | OpenACC OpenCL DirectCompute |
| Form Factor | Plug-in Card |
The card is supplied as a bare plug-in unit with a passive cooler; power cables, mounting brackets and any server rails are not included and must be sourced separately.
This specific Lenovo ThinkSystem part is the 32 GB HBM2 model; other capacity variants exist in the wider Tesla V100 line but are separate part numbers.
The 32 GB HBM2 memory provides high bandwidth for large-model training and HPC jobs, while the passive cooler requires adequate server airflow to sustain full performance.
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