534916-B21: HP 512MB Flash Backed Write Cache Memory Module
MPN: 534916-B21|By: HP
Starting from
$125
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 534916-B21 as a 512MB flash backed write cache Memory Module. 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 512MB module. Send the current layout and we will map the slots for you.
Quotes are itemised down to the accessories, so nothing that is needed to make the part work is left off and discovered later.
Specifications
General
- Manufacturer part number
- 534916-B21
- Product Type
- Server Memory
- Manufacturer
- HP
Technical specifications
- Capacity
- 512MB
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 HP 534916-B21 is deployed
- Like-for-like replacement of 534916-B21 in an existing system
- Standardising a host on one module type and speed
- Replacing a DIMM flagged by correctable-error logging
Who this part is for
- Procurement buying memory kits against a documented configuration
- MSPs matching DIMMs to what a customer already has installed
- Teams retiring mixed-speed memory in favour of one consistent part
What you get ordering this from us
Ordered by part number
Quoted and supplied against 534916-B21 specifically, so what arrives is what your build sheet asks for.
Compatibility checked
We confirm the part is supported on your platform before it ships, rather than leaving the check to you.
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.
Questions buyers ask about this part
Is 534916-B21 genuine and is the warranty intact?
Yes. It ships new in HP 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 534916-B21 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. We quote against a purchase order and hold the price for the quote period, with tiered rates at quantity. Net terms are available once the account is approved.
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.