809085-091: HPE 64GB DDR4 2400MHz ECC RDIMM Memory Module
MPN: 809085-091|By: HPE
Starting from
$465
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
Part number 809085-091 identifies this HPE 64GB DDR4 2400MHz 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
- 809085-091
- Product Type
- Server Memory
- Manufacturer
- HPE
Technical specifications
- Capacity
- 64GB
- Memory Type
- DDR4
- Module Type
- ECC
Published specifications for 809085-091. 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 809085-091 is deployed
- Like-for-like replacement of 809085-091 in an existing system
- Raising host memory to fit more virtual machines per node
- Filling the remaining channels on a part-populated board
Who this part is for
- MSPs matching DIMMs to what a customer already has installed
- 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 809085-091 specifically, so what arrives is what your build sheet asks for.
Volume pricing
The listed price is per unit; tiered pricing applies at quantity and purchase orders are accepted.
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 809085-091 genuine and is the warranty intact?
Yes, new and sealed, with the HPE warranty and the firmware update path intact. We do not sell refurbished stock as new, and we do not source grey-market units.
Will 809085-091 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, POs from qualified organisations, net terms on approval, and banded pricing once the quantity is worth banding. Send the number and we will price it properly.
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.