NVIDIA GeForce RTX 5090 32G Turbo vs NVIDIA Tesla A100 40G
The short answer
NVIDIA GeForce RTX 5090 32G Turbo wins this comparison with a score of 69.4/100, ahead of NVIDIA Tesla A100 40G (69.0).
The main differences: VRAM (32 GB vs 40 GB) · Bus width (512 bit vs 5120 bit).
Scores are computed from 7 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
| NVIDIA GeForce RTX 5090 32G Turbo | NVIDIA Tesla A100 40G | |
|---|---|---|
| Performance | 85 | 47 |
| Memory | 41 | 83 |
| Power | 68 | 66 |
How to choose
Pick the NVIDIA Tesla A100 40G if you care about…
- VRAM 40 GB +8 GB vs 32 GB
- Bus width 5120 bit +4608 bit vs 512 bit
Best price now
Cheap alternatives
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Where they differ
2 · Only rows that actually differ, with a verdict per row
| NVIDIA GeForce RTX 5090 32G Turbo | NVIDIA Tesla A100 40G | Where they differ | |
|---|---|---|---|
| VRAM | 32 GB | 40 GB | NVIDIA Tesla A100 40G |
| Bus width | 512 bit | 5120 bit | NVIDIA Tesla A100 40G |
The record
Grouped and collapsible · Tap a group to fold
Performance4▾
| NVIDIA GeForce RTX 5090 32G Turbo | NVIDIA Tesla A100 40G | |
|---|---|---|
| Shader cores | 21760 | — |
| Boost clock | 2.4 GHz | — |
| Memory bandwidth | 1792 GB/s | — |
| Base clock | 2.0 GHz | — |
Memory2▾
| NVIDIA GeForce RTX 5090 32G Turbo | NVIDIA Tesla A100 40G | |
|---|---|---|
| VRAM | 32 GB | 40 GB |
| Bus width | 512 bit | 5120 bit |
Power1▾
| NVIDIA GeForce RTX 5090 32G Turbo | NVIDIA Tesla A100 40G | |
|---|---|---|
| TDP | — | 250 W |
Data sources: GSMArena
FAQ
Which is better, NVIDIA GeForce RTX 5090 32G Turbo or NVIDIA Tesla A100 40G?
NVIDIA GeForce RTX 5090 32G Turbo edges ahead 69 to 69. Choose NVIDIA Tesla A100 40G instead if VRAM matters more to you.
What are the main differences?
The main differences: VRAM (32 GB vs 40 GB) · Bus width (512 bit vs 5120 bit).
How are the scores calculated?
We normalize 7 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.