RTX 3050 8GBBudget pick8 GB VRAM (+2)
B
Adds memory headroom for longer context windows and future model growth.48 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Nemotron Mini 4B needs ~5.9 GB VRAM. GTX 1660 Super 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
Fit status
Runs with offload
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
4K
Memory
5.9 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 | B | Runs well | 56.0 tok/s | 1886 ms | 4K |
| Coding | C | Runs with offload | 56.0 tok/s | 3457 ms | 4K |
| Agentic Coding | F | Too heavy | 32.9 tok/s | 8547 ms | 4K |
| Reasoning | C | Runs with offload | 56.0 tok/s | 4086 ms | 4K |
| RAG | F | Too heavy | 32.9 tok/s | 10683 ms | 4K |
How Nemotron Mini 4B (4B params) fits at each quantization level on GTX 1660 Super 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 5060 8GBBest valueRaises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeRaises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
Yes, GTX 1660 Super 6GB can run Nemotron Mini 4B with a C grade (Runs with offload). Expected decode speed: 56.0 tok/s.
Nemotron Mini 4B (4B parameters) requires approximately 5.9 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 GTX 1660 Super 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 GTX 1660 Super 6GB receives a C grade with 56.0 tok/s and 4K context.
On GTX 1660 Super 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-gtx-1660-super-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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.