Can Phi 4 Mini 4B run on GTX 1060 6GB?
YES — With Offload
Phi 4 Mini 4B needs ~5.7 GB VRAM. GTX 1060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~46 tok/s.
Operating mode
Choose the run profile you care about
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
Runs with offload
Decode
49.9 tok/s
TTFT
3879 ms
Safe context
19K
Memory
5.7 GB / 6.0 GB
Memory breakdown
See how fast it feels
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 46.4 tok/s | 2275 ms | 19K |
| Coding | A | Runs with offload | 46.4 tok/s | 4170 ms | 19K |
| Agentic Coding | C | Very compromised | 22.9 tok/s | 12296 ms | 19K |
| Reasoning | A | Runs with offload | 46.4 tok/s | 4928 ms | 19K |
| RAG | C | Very compromised | 22.9 tok/s | 15370 ms | 19K |
Quantization options
How Phi 4 Mini 4B (4B params) fits at each quantization level on GTX 1060 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 |
Get started
Copy-paste commands to run Phi 4 Mini 4B on your machine.
Run
ollama run phi4-miniYour hardware
