FX698AA: HP 1GB DDR3 1333MHz ECC 240-Pin UDIMM Memory
MPN: FX698AA|By: HP
Starting from
$80
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
HP lists FX698AA as a 1GB DDR3 1333MHz ECC 240-Pin UDIMM Memory. 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.
The listed price is per unit and current; tell us the quantity and we will confirm what it holds at before you raise the order.
Specifications
General
- Manufacturer part number
- FX698AA
- Product Type
- Server Memory
- Manufacturer
- HP
Technical specifications
- Capacity
- 1GB
- Memory Type
- DDR3
- Module Type
- ECC
Published specifications for FX698AA. 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 HP FX698AA is deployed
- Like-for-like replacement of FX698AA 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
- Virtualisation teams raising host memory to fit more guests per node
What you get ordering this from us
Ordered by part number
Quoted and supplied against FX698AA 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.
No substitutions without asking
If we cannot supply the exact code we tell you what we can supply and why, rather than shipping an equivalent and letting you find out.
Questions buyers ask about this part
Is FX698AA genuine and is the warranty intact?
Yes. Supplied new and unopened, so the manufacturer warranty and firmware update path stay supported. No grey-market stock and no refurbished units sold as new.
Will FX698AA 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 |
| HPE 16GB DDR5-4800 PC5-38400 ECC RDIMM CL40 288-Pin Server Memory Module | HPE | 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 |
Browse all Server Memory or everything we stock from HP.
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.