15R9379: IBM 8GB CDIMM 276-Pin Memory Module
MPN: 15R9379|By: IBM

Starting from
$120
Indicative. Final pricing is confirmed with your quote.
In Stockships worldwide
- Condition:
- New (authorized channel)
- Warranty:
- Full manufacturer warranty
Prefer your own inbox? Email us about this part
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
This is the IBM 8GB CDIMM 276-Pin Memory Module, order code 15R9379. Registered, load-reduced and unbuffered modules are not interchangeable, and a board that accepts one will refuse to post on another. Send the server model and we will check it against the supported-DIMM list before you order.
If the part is wrong for your platform we will say so and suggest the one that is, which is cheaper for both of us than a return.
Specifications
General
- Manufacturer part number
- 15R9379
- Product Type
- Server Memory
- Manufacturer
- IBM
Technical specifications
- Capacity
- 8GB
These are the 3 facts we can state about 15R9379 without inferring anything. Compatibility beyond this is confirmed per platform, on request, before you order.
Where the IBM 15R9379 is deployed
- Swapping out a failed server memory module for 15R9379 under change control
- Standardising a host on one module type and speed
- Replacing a DIMM flagged by correctable-error logging
Who this part is for
- MSPs matching DIMMs to what a customer already has installed
- Teams retiring mixed-speed memory in favour of one consistent part
- Virtualisation teams raising host memory to fit more guests per node
What you get ordering this from us
Ordered by part number
The order is placed against 15R9379, not against a description of it, so there is no room for a substitution you did not agree to.
Answered by a human
A quote comes back with the reasoning attached, including the cases where we think you should buy something else.
Volume pricing
The listed price is per unit; tiered pricing applies at quantity and purchase orders are accepted.
Questions buyers ask about this part
Is 15R9379 genuine and is the warranty intact?
Yes, new and sealed, with the IBM warranty and the firmware update path intact. We do not sell refurbished stock as new, and we do not source grey-market units.
Will 15R9379 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 IBM.
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.
