Phi 3.5 Mini 4B needs ~10.1 GB but RTX 4050 Laptop 6GB only has 6.0 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
4.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.4 tok/s
TTFT
13447 ms
Safe context
5K
Memory
10.1 GB / 6.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 10.1 GB, but this setup only exposes 6.0 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 | C | Very compromised (needs ~0.4 GB host RAM) | 29.6 tok/s | 3566 ms | 5K |
| Coding | F | Too heavy | 14.4 tok/s | 13447 ms | 5K |
| Agentic Coding | F | Too heavy | 8.6 tok/s | 32682 ms | 5K |
| Reasoning | F | Too heavy | 14.4 tok/s | 15892 ms | 5K |
| RAG | F | Too heavy | 8.6 tok/s | 40852 ms | 5K |
How Phi 3.5 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 | B70 |
Q3_K_S | 3 | 2.0 GB | Low | A70 |
NVFP4 | 4 |
Upgrade options
RTX 3060 12GBBudget pickMakes 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.
~$329 MSRP
RTX 5060 Ti 16GBBest valueMakes 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.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeMakes 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.
~$499 MSRP
No, Phi 3.5 Mini 4B requires more memory than RTX 4050 Laptop 6GB provides.
Phi 3.5 Mini 4B (4B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4050 Laptop 6GB, Phi 3.5 Mini 4B achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13447ms using Q4_K_M quantization.
For coding workloads, Phi 3.5 Mini 4B on RTX 4050 Laptop 6GB receives a F grade with 14.4 tok/s and 5K context.
On RTX 4050 Laptop 6GB, Phi 3.5 Mini 4B can safely use up to 5K 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-3.5-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>
Preview:
2.2 GB |
| Medium |
| A70 |
Q4_K_M | 4 | 2.4 GB | Medium | A70 |
Q5_K_M | 5 | 2.9 GB | High | B70 |
Q6_KBest for your GPU | 6 | 3.3 GB | High | B69 |
Q8_0 | 8 | 4.3 GB | Very High | F0 |
F16 | 16 | 8.2 GB | Maximum | F0 |
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.
