Phi-4-reasoning-plus 14B needs ~14.1 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~26 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
2.1 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.3 GB host RAM)
Decode
25.5 tok/s
TTFT
7579 ms
Safe context
5K
Memory
14.1 GB / 12.0 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.
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 | S | Runs with offload (needs ~0.4 GB host RAM) | 32.5 tok/s | 3249 ms | 5K |
| Coding | A | Very compromised (needs ~1.3 GB host RAM) | 25.5 tok/s | 7579 ms | 5K |
| Agentic Coding | F | Too heavy | 16.9 tok/s | 16643 ms | 5K |
| Reasoning | A | Very compromised (needs ~1.3 GB host RAM) | 25.5 tok/s | 8957 ms | 5K |
| RAG | F | Too heavy | 16.9 tok/s | 20803 ms |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | S92 |
Q3_K_S | 3 | 7.2 GB | Low | S92 |
NVFP4Best for your GPU |
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoningYes, RTX 4070 Super 12GB can run Phi-4-reasoning-plus 14B with a A grade (Very compromised (needs ~1.3 GB host RAM)). Expected decode speed: 25.5 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Super 12GB, Phi-4-reasoning-plus 14B achieves approximately 25.5 tokens per second decode speed with a time-to-first-token of 7579ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on RTX 4070 Super 12GB receives a A grade with 25.5 tok/s and 5K context.
On RTX 4070 Super 12GB, Phi-4-reasoning-plus 14B can safely use up to 5K tokens of context. The model's official context limit is 33K, 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-reasoning-plus-14b-on-rtx-4070-super-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 5K |
| 4 |
8.2 GB |
| Medium |
| S91 |
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 |
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.