Gemma 3 4B needs ~6.1 GB but RTX 3050 Ti Laptop 4GB only has 4.0 GB. Try a smaller quantization or lighter model.
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
2.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
19.8 tok/s
TTFT
9788 ms
Safe context
4K
Memory
6.1 GB / 4.0 GB
Offload
30%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 6.1 GB, but this setup only exposes 4.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 29.3 tok/s | 3610 ms | 4K |
| Coding | F | Too heavy | 19.8 tok/s | 9788 ms | 4K |
| Agentic Coding | F | Too heavy | 10.7 tok/s | 26336 ms | 4K |
| Reasoning | F | Too heavy | 19.8 tok/s | 11568 ms | 4K |
| RAG | F | Too heavy | 10.7 tok/s | 32920 ms | 4K |
How Gemma 3 4B (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 | A76 |
Q3_K_S | 3 | 2.0 GB | Low | F0 |
NVFP4 | 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 |
アップグレードオプション
GTX 1660 Super 6GBコスパ重視Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$229 MSRP
RTX 3050 8GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$249 MSRP
GTX 1060 6GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$249 MSRP
No, Gemma 3 4B requires more memory than RTX 3050 Ti Laptop 4GB provides.
Gemma 3 4B (4B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, Gemma 3 4B achieves approximately 19.8 tokens per second decode speed with a time-to-first-token of 9788ms using Q4_K_M quantization.
For coding workloads, Gemma 3 4B on RTX 3050 Ti Laptop 4GB receives a F grade with 19.8 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, Gemma 3 4B can safely use up to 4K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-3-4b-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>
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