Nemotron 3 Nano 30B needs ~24.0 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~8 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
Runs with offload (needs ~0 GB host RAM)
Decode
8.7 tok/s
TTFT
22217 ms
Safe context
16K
Memory
24.0 GB / 24.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload | 12.0 tok/s | 8806 ms | 16K |
| Coding | S | Runs with offload | 8.1 tok/s | 23884 ms | 16K |
| Agentic Coding | A | Very compromised (needs ~1.7 GB host RAM) | 7.0 tok/s | 39963 ms | 16K |
| Reasoning | S | Runs with offload (needs ~0 GB host RAM) | 8.7 tok/s | 26257 ms | 16K |
| RAG | A | Very compromised (needs ~1.7 GB host RAM) | 7.0 tok/s | 49954 ms |
How Nemotron 3 Nano 30B (30B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | S90 |
Q3_K_S | 3 | 14.7 GB | Low | S90 |
NVFP4 | 4 |
Copy-paste commands to run Nemotron 3 Nano 30B on your machine.
Run
ollama run nemotron-nano:30bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 30.9 tok/s | ||
| 35B | A | 12.7 tok/s |
Yes, Tesla P40 24GB can run Nemotron 3 Nano 30B with a S grade (Runs with offload). Expected decode speed: 8.1 tok/s.
Nemotron 3 Nano 30B (30B parameters) requires approximately 24.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron 3 Nano 30B is Q4_K_M, which balances quality and memory efficiency.
On Tesla P40 24GB, Nemotron 3 Nano 30B achieves approximately 8.1 tokens per second decode speed with a time-to-first-token of 23884ms using Q4_K_M quantization.
For coding workloads, Nemotron 3 Nano 30B on Tesla P40 24GB receives a S grade with 8.1 tok/s and 16K context.
On Tesla P40 24GB, Nemotron 3 Nano 30B can safely use up to 16K 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-3-nano-30b-on-tesla-p40-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 16K |
16.8 GB |
| Medium |
| S90 |
Q4_K_MBest for your GPU | 4 | 18.3 GB | Medium | S89 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 GB | Maximum | F0 |
| 35B | A | 17.1 tok/s |
| 32B | A | 6.5 tok/s |
| 30.5B | S | 30.9 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.