Phi-4-reasoning-plus 14B needs ~14.8 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~19 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
Tight fit
Decode
18.7 tok/s
TTFT
10352 ms
Safe context
22K
Memory
14.8 GB / 16.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Tight fit | 18.7 tok/s | 5646 ms | 22K |
| Coding | S | Tight fit | 18.7 tok/s | 10352 ms | 22K |
| Agentic Coding | A | Very compromised (needs ~0.9 GB host RAM) | 11.1 tok/s | 25337 ms | 22K |
| Reasoning | S | Tight fit | 18.7 tok/s | 12234 ms | 22K |
| RAG | A | Very compromised (needs ~0.9 GB host RAM) | 11.1 tok/s | 31672 ms | 22K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | S89 |
Q3_K_S | 3 | 7.2 GB | Low | S91 |
NVFP4 | 4 | 8.2 GB | Medium | S91 |
Q4_K_M | 4 | 9.0 GB | Medium | S91 |
Q5_K_M | 5 | 10.6 GB | High | S91 |
Q6_KBest for your GPU | 6 | 12.1 GB | High | S90 |
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-reasoningYes, NVIDIA A2 16GB can run Phi-4-reasoning-plus 14B with a S grade (Tight fit). Expected decode speed: 18.7 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.8 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 NVIDIA A2 16GB, Phi-4-reasoning-plus 14B achieves approximately 18.7 tokens per second decode speed with a time-to-first-token of 10352ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on NVIDIA A2 16GB receives a S grade with 18.7 tok/s and 22K context.
On NVIDIA A2 16GB, Phi-4-reasoning-plus 14B can safely use up to 22K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-reasoning-plus-14b-on-a2-16gb" 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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