Nemotron Nano 9B v2 needs ~10.3 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~48 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
48.0 tok/s
TTFT
4031 ms
Safe context
27K
Memory
10.3 GB / 12.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 48.0 tok/s | 2199 ms | 27K |
| Coding | A | Tight fit | 48.0 tok/s | 4031 ms | 27K |
| Agentic Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 31.6 tok/s | 8915 ms | 27K |
| Reasoning | A | Tight fit | 48.0 tok/s | 4764 ms | 27K |
| RAG | B | Runs with offload (needs ~0.3 GB host RAM) | 31.6 tok/s | 11144 ms |
How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | A79 |
Q3_K_S | 3 | 4.4 GB | Low | A81 |
NVFP4 | 4 |
Copy-paste commands to run Nemotron Nano 9B v2 on your machine.
Run
ollama run nemotron-nano:9b-v2Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 14B | A | 18.5 tok/s | ||
| 14B | A | 18.4 tok/s |
Yes, RTX 3500 Ada Laptop 12GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 48.0 tok/s.
Nemotron Nano 9B v2 (9B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.
On RTX 3500 Ada Laptop 12GB, Nemotron Nano 9B v2 achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4031ms using Q4_K_M quantization.
For coding workloads, Nemotron Nano 9B v2 on RTX 3500 Ada Laptop 12GB receives a A grade with 48.0 tok/s and 27K context.
On RTX 3500 Ada Laptop 12GB, Nemotron Nano 9B v2 can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nemotron-nano-9b-v2-on-rtx-3500-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 27K |
5.0 GB |
| Medium |
| A81 |
Q4_K_M | 4 | 5.5 GB | Medium | A82 |
Q5_K_M | 5 | 6.5 GB | High | A82 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | A81 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
| 14B | A | 16.8 tok/s |
| 14B | B | 17.2 tok/s |