RTX 3050 8GBBudget pick8 GB VRAM (+2)
B
Adds memory headroom for longer context windows and future model growth.56 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Nemotron Mini 4B needs ~6.2 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~56 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
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
4K
Memory
6.2 GB / 6.0 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 | C | Tight fit | 56.0 tok/s | 1886 ms | 4K |
| Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 56.0 tok/s | 3457 ms | 4K |
| Agentic Coding | F | Too heavy | 31.4 tok/s | 8955 ms | 4K |
| Reasoning | C | Runs with offload (needs ~0.1 GB host RAM) | 56.0 tok/s | 4086 ms | 4K |
| RAG | F | Too heavy | 31.4 tok/s | 11194 ms |
How Nemotron Mini 4B (4B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C55 |
Q3_K_S | 3 | 2.0 GB | Low | B55 |
NVFP4 | 4 |
Copy-paste commands to run Nemotron Mini 4B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "nvidia/Nemotron-Mini-4B-Instruct" \
--hf-file "Nemotron-Mini-4B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 3050 8GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$249 MSRP
RTX 4060 8GBBest valueAdds memory headroom for longer context windows and future model growth.
~$299 MSRP
RTX 5060 8GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$299 MSRP
Yes, RTX 2060 6GB can run Nemotron Mini 4B with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 56.0 tok/s.
Nemotron Mini 4B (4B parameters) requires approximately 6.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 6GB, Nemotron Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, Nemotron Mini 4B on RTX 2060 6GB receives a C grade with 56.0 tok/s and 4K context.
On RTX 2060 6GB, Nemotron Mini 4B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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-mini-4b-on-rtx-2060-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
2.2 GB |
| Medium |
| B55 |
Q4_K_M | 4 | 2.4 GB | Medium | B55 |
Q5_K_M | 5 | 2.9 GB | High | C55 |
Q6_KBest for your GPU | 6 | 3.3 GB | High | C55 |
Q8_0 | 8 | 4.3 GB | Very High | F0 |
F16 | 16 | 8.2 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.