Gemma 3 4B needs ~6.3 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~41 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
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
40.6 tok/s
TTFT
4767 ms
Safe context
14K
Memory
6.3 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 | A | Tight fit | 56.0 tok/s | 1886 ms | 14K |
| Coding | A | Runs with offload (needs ~0.1 GB host RAM) | 40.6 tok/s | 4767 ms | 14K |
| Agentic Coding | F | Too heavy | 22.3 tok/s | 12610 ms | 14K |
| Reasoning | A | Runs with offload (needs ~0.1 GB host RAM) | 40.6 tok/s | 5633 ms | 14K |
| RAG | F | Too heavy | 22.3 tok/s | 15762 ms | 14K |
How Gemma 3 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 | A75 |
Q3_K_S | 3 | 2.0 GB | Low | A76 |
NVFP4 | 4 | 2.2 GB | Medium | A76 |
Q4_K_M | 4 | 2.4 GB | Medium | A76 |
Q5_K_M | 5 | 2.9 GB | High | A75 |
Q6_KBest for your GPU | 6 | 3.3 GB | High | A75 |
Q8_0 | 8 | 4.3 GB | Very High | F0 |
F16 | 16 | 8.2 GB | Maximum | F0 |
Copy-paste commands to run Gemma 3 4B on your machine.
Run
ollama run gemma3:4bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 2.5 VL 7B | 7B | B | 19.8 tok/s | |
| Qwen 2.5 7B | 7B | B | 19.8 tok/s | |
 Codestral Mamba 7B | 7B | B | 20.4 tok/s | |
 Gemma 4 E2B | 5.1B | A | 49 tok/s |
Yes, RTX 4050 Laptop 6GB can run Gemma 3 4B with a A grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 40.6 tok/s.
Gemma 3 4B (4B parameters) requires approximately 6.3 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 4050 Laptop 6GB, Gemma 3 4B achieves approximately 40.6 tokens per second decode speed with a time-to-first-token of 4767ms using Q4_K_M quantization.
For coding workloads, Gemma 3 4B on RTX 4050 Laptop 6GB receives a A grade with 40.6 tok/s and 14K context.
On RTX 4050 Laptop 6GB, Gemma 3 4B can safely use up to 14K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-3-4b-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>
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