RTX 3050 8GBBudget pick8 GB VRAM (+2)224 GB/s (+32)
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. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~57 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
56.8 tok/s
TTFT
3408 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.
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 | 56.8 tok/s | 1859 ms | 4K |
| Coding | C | Runs with offload | 56.8 tok/s | 3408 ms | 4K |
| Agentic Coding | F | Too heavy | 24.2 tok/s | 11625 ms | 4K |
| Reasoning | C | Runs with offload | 56.8 tok/s | 4027 ms | 4K |
| RAG | F | Too heavy | 24.2 tok/s | 14531 ms | 4K |
How Nemotron Mini 4B (4B params) fits at each quantization level on RTX 4050 Laptop 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 34%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeRaises estimated decode speed by about 34%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
Yes, RTX 4050 Laptop 6GB can run Nemotron Mini 4B with a C grade (Runs with offload). Expected decode speed: 56.8 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 RTX 4050 Laptop 6GB, Nemotron Mini 4B achieves approximately 56.8 tokens per second decode speed with a time-to-first-token of 3408ms using Q4_K_M quantization.
For coding workloads, Nemotron Mini 4B on RTX 4050 Laptop 6GB receives a C grade with 56.8 tok/s and 4K context.
On RTX 4050 Laptop 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-4050-laptop-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.