698555-001: HPE 4GB Flash Backed Write Cache 72-Bit FBWC Memory
MPN: 698555-001|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
The HPE 4GB flash backed write cache 72-Bit FBWC Memory is supplied under part number 698555-001. Population order decides whether you get the full memory bandwidth or a fraction of it, and the rules change between one and two processors. This is a 4GB module. Send the current layout and we will map the slots for you.
Quote requests are answered within 48 business hours, with availability confirmed before you raise a purchase order rather than after.
Specifications
General
- Manufacturer part number
- 698555-001
- Product Type
- Server Memory
- Manufacturer
- HPE
Technical specifications
- Capacity
- 4GB
The table below is what the server memory module is documented as, not what we have inferred from the part number. If the figure you need is missing, ask and we will source it from the manufacturer.
Where the HPE 698555-001 is deployed
- Sourcing 698555-001 against a build sheet that names it explicitly
- Standardising a host on one module type and speed
- Raising host memory to fit more virtual machines per node
Who this part is for
- Virtualisation teams raising host memory to fit more guests per node
- Teams retiring mixed-speed memory in favour of one consistent part
- Procurement buying memory kits against a documented configuration
What you get ordering this from us
Ordered by part number
Quoted and supplied against 698555-001 specifically, so what arrives is what your build sheet asks for.
Population mapped for you
Send the current DIMM layout and we return the slot map with the quote, so the modules go in the right order.
Rank and voltage confirmed
We check rank, voltage and module type against the platform before quoting, which is where most memory orders go wrong.
Questions buyers ask about this part
Is 698555-001 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 698555-001 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
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|---|---|---|---|---|
| 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.