840756-091: HPE 16GB DDR4 2666MHz ECC RDIMM Memory Module
MPN: 840756-091|By: HPE
Starting from
$210
Indicative. Final pricing is confirmed with your quote.
In Stockships worldwide
- Condition:
- New (authorized channel)
- Warranty:
- Full manufacturer warranty
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Bulk order? Request volume pricing
Authorized & warrantied
Sourced through authorized distribution, with the full manufacturer warranty.
Compatibility checked
Validated against your server platform before dispatch.
Delivery & lead time
Confirmed with your quote, including expedited freight where available.
Business & public sector
POs, net terms and volume pricing for qualified organizations, government and edu.
Product overview
HPE lists 840756-091 as a 16GB DDR4 2666MHz ECC RDIMM Memory Module. A channel runs at the speed of its slowest module, so a faster part fitted alongside an older one buys nothing. Tell us what is already installed and we will keep the channel consistent.
Send the quantity you need and we will come back within 48 business hours with availability confirmed, not estimated.
Specifications
General
- Manufacturer part number
- 840756-091
- Product Type
- Server Memory
- Manufacturer
- HPE
Technical specifications
- Capacity
- 16GB
- Memory Type
- DDR4
- Module Type
- ECC
Published specifications for 840756-091. Anything not listed here we will confirm against the manufacturer's documentation before quoting, because we would rather answer the question than let you assume.
Where the HPE 840756-091 is deployed
- Sourcing 840756-091 against a build sheet that names it explicitly
- Raising host memory to fit more virtual machines per node
- Standardising a host on one module type and speed
Who this part is for
- Virtualisation teams raising host memory to fit more guests per node
- Procurement buying memory kits against a documented configuration
- Engineers populating channels correctly on a new or part-filled board
What you get ordering this from us
Ordered by part number
Quoted and supplied against 840756-091 specifically, so what arrives is what your build sheet asks for.
Rank and voltage confirmed
We check rank, voltage and module type against the platform before quoting, which is where most memory orders go wrong.
Compatibility checked
We confirm the part is supported on your platform before it ships, rather than leaving the check to you.
Questions buyers ask about this part
Is 840756-091 genuine and is the warranty intact?
Yes. It ships new in HPE packaging, which is what keeps the warranty and the firmware entitlement valid. Anything we cannot supply that way we tell you about before you order.
Will 840756-091 work in my server?
Send the server model and a list of the DIMMs already fitted. Rank, voltage and population order all have to agree, and a module that is electrically compatible can still be unsupported in a given slot. We map the population before you order.
Do you accept purchase orders and quote at volume?
Yes. Purchase orders are accepted from qualified organisations, net terms are available on approval, and tiered pricing applies at quantity. Tell us how many you need.
Compare other Server Memory we stock
| Model | Brand | Key spec | Availability | From |
|---|---|---|---|---|
| SK hynix 32GB DDR5-5600 PC5-44800 ECC Registered CL46 288-Pin RDIMM Server Memory Module | SK hynix | Capacity: 32GB | In Stock | $215 |
| Samsung 16GB DDR4-3200 PC4-25600 ECC Registered CL22 288-Pin RDIMM Server Memory Module | Samsung | Capacity: 16GB | In Stock | $55 |
| Micron 128GB DDR4-3200 PC4-25600 ECC Load-Reduced CL22 288-Pin LRDIMM Server Memory Module | Micron | Capacity: 128GB | In Stock | $500 |
| Dell Technologies 16GB DDR5-4800 PC5-38400 ECC RDIMM CL40 288-Pin Server Memory Module | Dell Technologies | Capacity: 16GB | In Stock | $95 |
| Lenovo 16GB DDR5-4800 PC5-38400 ECC RDIMM CL40 288-Pin Server Memory Module | Lenovo | Capacity: 16GB | In Stock | $90 |
| Kingston 8GB DDR4-3200 PC4-25600 ECC RDIMM CL22 288-Pin Server Memory Module | Kingston | Capacity: 8GB | In Stock | $35 |
Browse all Server Memory or everything we stock from HPE.
Specifying a system, not just the part?
Host memory on an AI node is normally sized against accelerator memory, so the model and its working set both fit.