2076-AHCB: IBM 64GB Memory Cache Memory Upgrade
MPN: 2076-AHCB|By: IBM
Price
Quoted on request
Current pricing within 48 business hours.
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 IBM 64GB Memory cache Memory upgrade is supplied under part number 2076-AHCB. 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 64GB module. Send the current layout and we will map the slots for you.
No price is published here because the market for this part moves week to week; ask and we will give you a current one rather than a stale one.
Specifications
General
- Manufacturer part number
- 2076-AHCB
- Product Type
- Server Memory
- Manufacturer
- IBM
Technical specifications
- Capacity
- 64GB
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 IBM 2076-AHCB is deployed
- Swapping out a failed server memory module for 2076-AHCB under change control
- Filling the remaining channels on a part-populated board
- Standardising a host on one module type and speed
Who this part is for
- Procurement buying memory kits against a documented configuration
- Virtualisation teams raising host memory to fit more guests per node
- 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 2076-AHCB 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.
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 2076-AHCB 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 2076-AHCB 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 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.