Samsung 990 PRO NVMe M.2 2TB vs ZhiTai TiPlus9100 2TB
ZhiTai TiPlus9100 2TB — Pick the ZhiTai TiPlus9100 2TB — it leads these groups: Performance 2
Tap a group name to see the proof · The scoring method is public
The short answer
ZhiTai TiPlus9100 2TB wins this comparison with a score of 64/100, ahead of Samsung 990 PRO NVMe M.2 2TB (55).
The main differences: Read speed (7450 MB/s vs 12000 MB/s) · Write speed (6900 MB/s vs 10700 MB/s).
Scores are computed from 4 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
| Samsung 990 PRO NVMe M.2 2TB | ZhiTai TiPlus9100 2TB | |
|---|---|---|
| Capacity | 47 | 47 |
| Performance | 65 | 86 |
How to choose
Pick the ZhiTai TiPlus9100 2TB if you care about…
- Read speed 12000 MB/s +4550 MB/s vs 7450 MB/s
- Write speed 10700 MB/s +3800 MB/s vs 6900 MB/s
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Where they differ
2 · Only rows that actually differ, with a verdict per row
| Samsung 990 PRO NVMe M.2 2TB | ZhiTai TiPlus9100 2TB | Where they differ | |
|---|---|---|---|
| Read speed | 7450 MB/s | 12000 MB/s | ZhiTai TiPlus9100 2TB |
| Write speed | 6900 MB/s | 10700 MB/s | ZhiTai TiPlus9100 2TB |
The record
Grouped and collapsible · Tap a group to fold
Capacity1▾
| Samsung 990 PRO NVMe M.2 2TB | ZhiTai TiPlus9100 2TB | |
|---|---|---|
| Capacity | 2048 GB | 2048 GB |
Design1▾
| Samsung 990 PRO NVMe M.2 2TB | ZhiTai TiPlus9100 2TB | |
|---|---|---|
| Form factor | — | M.2 2280 |
Performance2▾
| Samsung 990 PRO NVMe M.2 2TB | ZhiTai TiPlus9100 2TB | |
|---|---|---|
| Read speed | 7450 MB/s | 12000 MB/s |
| Write speed | 6900 MB/s | 10700 MB/s |
Data sources: GSMArena
FAQ
Which is better, Samsung 990 PRO NVMe M.2 2TB or ZhiTai TiPlus9100 2TB?
ZhiTai TiPlus9100 2TB has the higher overall score (64 vs 55) and leads in Performance.
What are the main differences?
The main differences: Read speed (7450 MB/s vs 12000 MB/s) · Write speed (6900 MB/s vs 10700 MB/s).
How are the scores calculated?
We normalize 4 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.
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