Phi-4 Mini Reasoning 4B needs ~5.6 GB VRAM. GTX 1060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~53 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
52.5 tok/s
TTFT
3685 ms
Safe context
21K
Memory
5.6 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | S | Runs well | 52.5 tok/s | 2010 ms | 21K |
| Coding | S | Tight fit | 52.5 tok/s | 3685 ms | 21K |
| Agentic Coding | A | Very compromised (needs ~0.3 GB host RAM) | 26.9 tok/s | 10464 ms | 21K |
| Reasoning | S | Tight fit | 52.5 tok/s | 4355 ms | 21K |
| RAG | A | Very compromised (needs ~0.3 GB host RAM) | 26.9 tok/s | 13080 ms | 21K |
How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on GTX 1060 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 | A | 31.2 tok/s |
Yes, GTX 1060 6GB can run Phi-4 Mini Reasoning 4B with a S grade (Tight fit). Expected decode speed: 52.5 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 5.6 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 GTX 1060 6GB, Phi-4 Mini Reasoning 4B achieves approximately 52.5 tokens per second decode speed with a time-to-first-token of 3685ms using Q4_K_M quantization.
For coding workloads, Phi-4 Mini Reasoning 4B on GTX 1060 6GB receives a S grade with 52.5 tok/s and 21K context.
On GTX 1060 6GB, Phi-4 Mini Reasoning 4B can safely use up to 21K tokens of context. The model's official context limit is 131K, 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/phi-4-mini-reasoning-on-gtx-1060-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: