NEMIX RAM DDR5-5600 2TB (16x128GB) ECC RDIMM CL46 1.1V Supermicro H14SST-G memory upgrade
- 2TB (16 x 128GB)
- 288-Pin DDR5 RDIMM
Sprejemamo kriptovalute — tako lahko sredstva, ki jih imate, kupijo pravo strojno opremo.Plačajte v kriptovaluti, zapravite jo za pravo strojno opremo. Brezplačna zavarovana dostava po vsem svetu nad 399 $. Nevtralni kartoni brez znamke, poslani iz Nemčije.Brezplačna zavarovana dostava nad 399 $. Vaš tečaj je zaklenjen za 30 minut, takoj ko se odpre blagajna.Tečaj zaklenjen 30 minut. Vsak izdelek je zapečaten, njegova serijska številka preverjena, in krit z 24-mesečno garancijo.Zapečateno, 24-mesečna garancija.
NVIDIA
A GPU accelerator card with 24 GB GDDR5 memory and a PCI Express 3.0 interface for compute workloads
The NVIDIA Tesla M40 is a GPU accelerator card equipped with 24GB of GDDR5 memory and a PCI Express 3.0 interface. It is built for compute-intensive workloads such as deep learning training, scientific simulation and large-scale data analytics. The PG600 board design targets server and workstation environments that need sustained double-precision throughput.
This card suits researchers and engineers who need high memory capacity for model training or batch inference in headless servers. It is not intended for users who require video outputs for monitors, workstations with limited auxiliary power connectors, or systems that cannot accommodate a dual-slot passive design.
The 24GB GDDR5 frame buffer is the specification that determines whether the M40 fits the workload. Models that need larger datasets resident in GPU memory benefit directly from this capacity, while smaller models may not require the full allocation.
| Memory Type | GDDR5 |
|---|
The card draws power through the PCI Express 3.0 slot and onboard auxiliary connectors; a supply with sufficient wattage and the correct 8-pin or 6-pin leads is needed.
It follows a dual-slot PCIe form factor, so verify that your case has two adjacent expansion slots and enough length clearance for a full-height board.
Large-model inference, batch rendering and scientific compute jobs that need high memory capacity without NVLink scaling.
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