Phi-4-reasoning-plus 14B needs ~12.6 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q3_K_S quantization, expect ~22 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
3.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.0 tok/s
TTFT
13834 ms
Safe context
4K
Memory
14.3 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 10% 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 0.9 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 ~1.3 GB host RAM) | 17.9 tok/s | 5892 ms | 4K |
| Coding | F | Too heavy | 14.0 tok/s | 13834 ms | 4K |
| Agentic Coding | F | Too heavy | 9.2 tok/s | 30756 ms | 4K |
| Reasoning | F | Too heavy | 14.0 tok/s | 16349 ms | 4K |
| RAG | F | Too heavy | 9.2 tok/s | 38445 ms | 4K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | S92 |
Q3_K_SBest for your GPU | 3 | 7.2 GB | Low | S92 |
NVFP4 | 4 | 8.2 GB | Medium | F0 |
Q4_K_M | 4 | 9.0 GB | Medium | F0 |
Q5_K_M | 5 | 10.6 GB | High | F0 |
Q6_K | 6 | 12.1 GB | High | F0 |
Q8_0 | 8 | 15.7 GB | Very High | F0 |
F16 | 16 | 30.1 GB | Maximum | F0 |
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoning升级选项
RTX 5060 Ti 16GB经济之选Makes 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 16GB最佳性价比Makes 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
RTX 2000 Ada 16GBNVIDIA 升级Makes 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.
~$625 MSRP
RTX 4090 24GB最大飞跃Makes 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.
~$1,599 MSRP
Yes, GTX 1080 Ti 11GB can run Phi-4-reasoning-plus 14B at Q3_K_S quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q4_K_M requires 14.3 GB which exceeds available memory, but at Q3_K_S it needs only 12.6 GB. Expected decode speed: 21.6 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.3 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q3_K_S using 12.6 GB.
The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q3_K_S, which uses 12.6 GB.
On GTX 1080 Ti 11GB, Phi-4-reasoning-plus 14B achieves approximately 21.6 tokens per second decode speed with a time-to-first-token of 8952ms using Q3_K_S quantization.
For coding workloads, Phi-4-reasoning-plus 14B on GTX 1080 Ti 11GB receives a F grade with 14.0 tok/s and 4K context.
On GTX 1080 Ti 11GB, Phi-4-reasoning-plus 14B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 33K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-reasoning-plus-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>
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