ADATA XPG D300 DDR5 6000 C30 16GB vs Corsair Vengeance RGB DDR5-6000 C30
Corsair Vengeance RGB DDR5-6000 C30 — Pick the Corsair Vengeance RGB DDR5-6000 C30 — it leads these groups: Specs 1
Tap a group name to see the proof · The scoring method is public
The short answer
Corsair Vengeance RGB DDR5-6000 C30 wins this comparison with a score of 66/100, ahead of ADATA XPG D300 DDR5 6000 C30 16GB (65).
The main differences: Module size (16 GB vs 32 GB).
Scores are computed from 5 public spec attributes; missing values stay marked unknown in tables and are imputed at the category median, never passed off as measured.
At a glance
0–100 per group, normalized within the category · Tap a group for details
How to choose
Pick the Corsair Vengeance RGB DDR5-6000 C30 if you care about…
- Module size 32 GB +16 GB vs 16 GB
Best price now
Cheap alternatives
Samsung 2×8GB DDR4 3200
ADATA 16GB DDR3 2400
Corsair 16GB DDR3 1600 CMD16GX3M2A1600C9
Corsair 16GB DDR4 3600 CM4X16GC3600C18K2D-CN
Corsair 16GB DDR5 5200
ADATA XPG D500G DDR5 7200 64GB 32GBx2
Where they differ
1 · Only rows that actually differ, with a verdict per row
| ADATA XPG D300 DDR5 6000 C30 16GB | Corsair Vengeance RGB DDR5-6000 C30 | Where they differ | |
|---|---|---|---|
| Module size | 16 GB | 32 GB | Corsair Vengeance RGB DDR5-6000 C30 |
The record
Grouped and collapsible · Tap a group to fold
Speed2▾
| ADATA XPG D300 DDR5 6000 C30 16GB | Corsair Vengeance RGB DDR5-6000 C30 | |
|---|---|---|
| Memory speed | 6000 MT/s | 6000 MT/s |
| CAS latency | — | 30 |
Specs3▾
| ADATA XPG D300 DDR5 6000 C30 16GB | Corsair Vengeance RGB DDR5-6000 C30 | |
|---|---|---|
| DDR generation | 5 | — |
| Module size | 16 GB | 32 GB |
| Voltage | — | 1.4 V |
Data sources: Amazon · GSMArena
FAQ
Which is better, ADATA XPG D300 DDR5 6000 C30 16GB or Corsair Vengeance RGB DDR5-6000 C30?
Corsair Vengeance RGB DDR5-6000 C30 edges ahead 66 to 65 and leads in Specs.
What are the main differences?
The main differences: Module size (16 GB vs 32 GB).
How are the scores calculated?
We normalize 5 public spec attributes within the category and weight them by group into a 0–100 score; missing values are inherited from spec-identical twins or imputed at the category median, so a sparse spec sheet never earns an advantage. The methodology is public.