GTX 1660 Super 6GBBudget pick6 GB VRAM (+2)336 GB/s (+144)
B
Raises estimated decode speed by about 61%.56 tok/s decode
Raises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
~$229 MSRP
gemma 3 4b it needs ~4.2 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~35 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
34.7 tok/s
TTFT
5573 ms
Safe context
9K
Memory
4.2 GB / 4.0 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 | C | Runs with offload | 48.0 tok/s | 2200 ms | 9K |
| Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 34.7 tok/s | 5573 ms | 9K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 27.8 tok/s | 10123 ms | 9K |
| Reasoning | C | Runs with offload (needs ~0.1 GB host RAM) | 34.7 tok/s | 6586 ms | 9K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 27.8 tok/s |
How gemma 3 4b it (4B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 1.6 GB | Low | B55 |
Q3_K_S | 3 | 2.0 GB | Low | F0 |
Copy-paste commands to run gemma 3 4b it on your machine.
Run
lms load hf-lmstudio-community--gemma-3-4b-it-gguf && lms server startUpgrade options
GTX 1660 Super 6GBBudget pickRaises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
~$229 MSRP
GTX 1060 6GBBest valueRaises estimated decode speed by about 34%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
RTX 3050 8GBNVIDIA upgradeRaises estimated decode speed by about 38%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Yes, RTX 3050 Ti Laptop 4GB can run gemma 3 4b it with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 34.7 tok/s.
gemma 3 4b it (4B parameters) requires approximately 4.2 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 4b it is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, gemma 3 4b it achieves approximately 34.7 tokens per second decode speed with a time-to-first-token of 5573ms using Q4_K_M quantization.
For coding workloads, gemma 3 4b it on RTX 3050 Ti Laptop 4GB receives a C grade with 34.7 tok/s and 9K context.
On RTX 3050 Ti Laptop 4GB, gemma 3 4b it can safely use up to 9K tokens of context. The model's official context limit is —, 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/hf-lmstudio-community--gemma-3-4b-it-gguf-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 12654 ms |
| 9K |
| 4 |
2.2 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 2.4 GB | Medium | F0 |
Q5_K_M | 5 | 2.9 GB | High | F0 |
Q6_K | 6 | 3.3 GB | High | F0 |
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.