NEMIX RAM DDR5-5600 2TB (16x128GB) ECC RDIMM CL46 1.1V Supermicro H14SST-G memory upgrade
- 2TB (16 x 128GB)
- 288-Pin DDR5 RDIMM
Prihvaćamo kriptovalute — tako kriptovalute koje posjedujete mogu kupiti pravi hardver.Plaćajte kriptovalutom, trošite je na pravi hardver. Besplatna osigurana dostava diljem svijeta iznad 399 $. Obični vanjski kartoni bez oznaka, otpremaju se iz Njemačke.Besplatna osigurana dostava iznad 399 $. Vaš je tečaj blokiran 30 minuta od otvaranja blagajne.Tečaj blokiran 30 minuta. Svaki je artikl zapečaćen, njegov serijski broj provjeren, i pokriven jamstvom od 24 mjeseca.Zapečaćen, jamstvo 24 mjeseca.
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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