RTX 3060 12GBBest value12 GB VRAM (+1)
B
Makes the model fit on the accelerator instead of staying completely out of reach.16.2 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$329 MSRP
Phi-4 14B needs ~13.2 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With NVFP4 quantization, expect ~20 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
2.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
15.7 tok/s
TTFT
12328 ms
Safe context
4K
Memory
13.9 GB / 11.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~0.9 GB host RAM) | 20.3 tok/s | 5208 ms | 4K |
| Coding | F | Too heavy | 15.7 tok/s | 12328 ms | 4K |
| Agentic Coding | F | Too heavy | 10.2 tok/s | 27734 ms | 4K |
| Reasoning | F | Too heavy | 15.7 tok/s | 14569 ms | 4K |
| RAG | F | Too heavy | 10.2 tok/s | 34667 ms | 4K |
How Phi-4 14B (14B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A84 |
Q3_K_S | 3 | 6.9 GB | Low | A84 |
NVFP4Best for your GPU |
Copy-paste commands to run Phi-4 14B on your machine.
Run
ollama run phi4Upgrade options
RTX 3060 12GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
~$329 MSRP
RTX 5060 Ti 16GBBudget 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.
~$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
Yes, GTX 1080 Ti 11GB can run Phi-4 14B at NVFP4 quantization (Very compromised (needs ~1.3 GB host RAM)). The recommended Q4_K_M requires 13.9 GB which exceeds available memory, but at NVFP4 it needs only 13.2 GB. Expected decode speed: 20.1 tok/s.
Phi-4 14B (14B parameters) requires approximately 13.9 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at NVFP4 using 13.2 GB.
The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is NVFP4, which uses 13.2 GB.
On GTX 1080 Ti 11GB, Phi-4 14B achieves approximately 20.1 tokens per second decode speed with a time-to-first-token of 9621ms using NVFP4 quantization.
For coding workloads, Phi-4 14B on GTX 1080 Ti 11GB receives a F grade with 15.7 tok/s and 4K context.
On GTX 1080 Ti 11GB, Phi-4 14B can safely use up to 5K tokens of context at NVFP4 quantization. The model's official context limit is 16K, 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-4-14b-on-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
7.8 GB |
| Medium |
| A83 |
Q4_K_M | 4 | 8.5 GB | Medium | F0 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.