Phi-4 Mini Reasoning 4B needs ~5.3 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~60 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
Tight fit
Decode
59.8 tok/s
TTFT
3237 ms
Safe context
24K
Memory
5.3 GB / 6.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 59.8 tok/s | 1766 ms | 24K |
| Coding | S | Tight fit | 59.8 tok/s | 3237 ms | 24K |
| Agentic Coding | A | Very compromised (needs ~0.3 GB host RAM) | 35.0 tok/s | 8039 ms | 24K |
| Reasoning | S | Tight fit | 59.8 tok/s | 3826 ms | 24K |
| RAG | A | Very compromised (needs ~0.3 GB host RAM) | 35.0 tok/s | 10049 ms | 24K |
How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | S91 |
Q3_K_S | 3 | 1.9 GB | Low | S92 |
NVFP4 | 4 | 2.1 GB | Medium | S92 |
Q4_K_M | 4 | 2.3 GB | Medium | S91 |
Q5_K_M | 5 | 2.7 GB | High | S91 |
Q6_KBest for your GPU | 6 | 3.1 GB | High | S91 |
Q8_0 | 8 | 4.1 GB | Very High | F0 |
F16 | 16 | 7.8 GB | Maximum | F0 |
Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 4B | 4B | S | 40.6 tok/s |
Yes, RTX 4050 Laptop 6GB can run Phi-4 Mini Reasoning 4B with a S grade (Tight fit). Expected decode speed: 59.8 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 5.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4050 Laptop 6GB, Phi-4 Mini Reasoning 4B achieves approximately 59.8 tokens per second decode speed with a time-to-first-token of 3237ms using Q4_K_M quantization.
For coding workloads, Phi-4 Mini Reasoning 4B on RTX 4050 Laptop 6GB receives a S grade with 59.8 tok/s and 24K context.
On RTX 4050 Laptop 6GB, Phi-4 Mini Reasoning 4B can safely use up to 24K tokens of context. The model's official context limit is 131K, 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/phi-4-mini-reasoning-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: