Phi-4-reasoning-plus 14B needs ~15.6 GB VRAM. RTX 3090 Ti 24GB has 24.0 GB. With Q4_K_M quantization, expect ~86 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
Runs well
Decode
85.8 tok/s
TTFT
2256 ms
Safe context
33K
Memory
15.6 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 85.8 tok/s | 1231 ms | 33K |
| Coding | S | Runs well | 85.8 tok/s | 2256 ms | 33K |
| Agentic Coding | S | Runs well | 85.8 tok/s | 3282 ms | 33K |
| Reasoning | S | Runs well | 79.8 tok/s | 2867 ms | 33K |
| RAG | S | Runs well | 85.8 tok/s | 4103 ms | 33K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | S86 |
Q3_K_S | 3 | 7.2 GB | Low | S86 |
NVFP4 | 4 |
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoningYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 108.2 tok/s | |
| Qwen 3.5 27B | 27B | S | 46.9 tok/s |
Yes, RTX 3090 Ti 24GB can run Phi-4-reasoning-plus 14B with a S grade (Runs well). Expected decode speed: 85.8 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 15.6 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 3090 Ti 24GB, Phi-4-reasoning-plus 14B achieves approximately 85.8 tokens per second decode speed with a time-to-first-token of 2256ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on RTX 3090 Ti 24GB receives a S grade with 85.8 tok/s and 33K context.
On RTX 3090 Ti 24GB, Phi-4-reasoning-plus 14B can safely use up to 33K 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-3090-ti-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
8.2 GB |
| Medium |
| S87 |
Q4_K_M | 4 | 9.0 GB | Medium | S88 |
Q5_K_M | 5 | 10.6 GB | High | S89 |
Q6_K | 6 | 12.1 GB | High | S90 |
Q8_0Best for your GPU | 8 | 15.7 GB | Very High | S90 |
F16 | 16 | 30.1 GB | Maximum | F0 |
| Qwen 3.6 27B | 27B | S | 47.1 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 111.9 tok/s |
| Qwen 3.5 35B A3B | 35B | A | 60.6 tok/s |