370-7973: Sun 1GB RDIMM 184-Pin Memory Module
MPN: 370-7973|By: Sun
Starting from
$210
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
370-7973 is a Sun 1GB RDIMM 184-Pin Memory Module. Memory is the part most often ordered wrong, because rank, voltage and population rules differ per platform and per slot. Give us the server model and the DIMMs already fitted and we will confirm the configuration is supported.
Tell us the deadline you are working to and we will say whether it is achievable before you commit to it internally.
Specifications
General
- Manufacturer part number
- 370-7973
- Product Type
- Server Memory
- Manufacturer
- Sun
Technical specifications
- Capacity
- 1GB
- Module Type
- RDIMM
Specifications for 370-7973 as published by Sun. Where a value is absent it is because we could not verify it, not because it does not exist. Ask and we will check.
Where the Sun 370-7973 is deployed
- Like-for-like replacement of 370-7973 in an existing system
- Replacing a DIMM flagged by correctable-error logging
- Standardising a host on one module type and speed
Who this part is for
- Virtualisation teams raising host memory to fit more guests per node
- MSPs matching DIMMs to what a customer already has installed
- Procurement buying memory kits against a documented configuration
What you get ordering this from us
Ordered by part number
370-7973 is what is quoted and 370-7973 is what ships; where an alternate code is genuinely the same part we name it rather than swap it silently.
Rank and voltage confirmed
We check rank, voltage and module type against the platform before quoting, which is where most memory orders go wrong.
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 370-7973 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 370-7973 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 Sun.
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.