Phi 4 Mini 4B needs ~5.4 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
Tight fit
Decode
56.8 tok/s
TTFT
3408 ms
Safe context
23K
Memory
5.4 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 | A | Runs well | 56.8 tok/s | 1859 ms | 23K |
| Coding | A | Tight fit | 56.8 tok/s | 3408 ms | 23K |
| Agentic Coding | B | Very compromised (needs ~0.3 GB host RAM) | 32.0 tok/s | 8788 ms | 23K |
| Reasoning | A | Tight fit | 56.8 tok/s | 4027 ms | 23K |
| RAG | B | Very compromised (needs ~0.3 GB host RAM) | 32.0 tok/s | 10985 ms | 23K |
How Phi 4 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 | A75 |
Q3_K_S | 3 | 2.0 GB | Low | A75 |
NVFP4 | 4 | 2.2 GB | Medium | A75 |
Q4_K_M | 4 | 2.4 GB | Medium | A75 |
Q5_K_M | 5 | 2.9 GB | High | A75 |
Q6_KBest for your GPU | 6 | 3.3 GB | High | A74 |
Q8_0 | 8 | 4.3 GB | Very High | F0 |
F16 | 16 | 8.2 GB | Maximum | F0 |
Copy-paste commands to run Phi 4 Mini 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 2.5 VL 7B | 7B | B | 20 tok/s | |
| Qwen 2.5 7B | 7B | B | 20 tok/s | |
 Codestral Mamba 7B | 7B | A | 25.9 tok/s | |
 Gemma 4 E2B | 5.1B | A | 32.8 tok/s |
Yes, RTX 4050 Laptop 6GB can run Phi 4 Mini 4B with a A grade (Tight fit). Expected decode speed: 56.8 tok/s.
Phi 4 Mini 4B (4B parameters) requires approximately 5.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 4 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4050 Laptop 6GB, Phi 4 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, Phi 4 Mini 4B on RTX 4050 Laptop 6GB receives a A grade with 56.8 tok/s and 23K context.
On RTX 4050 Laptop 6GB, Phi 4 Mini 4B can safely use up to 23K tokens of context. The model's official context limit is 128K, 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-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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