Can Phi-4-reasoning-plus 14B run on RTX A4000 16GB?
YES — Tight Fit
Phi-4-reasoning-plus 14B needs ~14.8 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Operating mode
Choose the run profile you care about
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
37.6 tok/s
TTFT
5150 ms
Safe context
22K
Memory
14.8 GB / 16.0 GB
Memory breakdown
See how fast it feels
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Tight fit | 37.6 tok/s | 2809 ms | 22K |
| Coding | S | Tight fit | 35.0 tok/s | 5536 ms | 22K |
| Agentic Coding | A | Very compromised (needs ~0.9 GB host RAM) | 22.3 tok/s | 12604 ms | 22K |
| Reasoning | S | Tight fit | 37.6 tok/s | 6086 ms | 22K |
| RAG | A | Very compromised (needs ~0.9 GB host RAM) | 22.3 tok/s | 15755 ms | 22K |
Quantization options
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on RTX A4000 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 |
Get started
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoningFrequently asked questions
Can RTX A4000 16GB run Phi-4-reasoning-plus 14B?
Yes, RTX A4000 16GB can run Phi-4-reasoning-plus 14B with a S grade (Tight fit). Expected decode speed: 35.0 tok/s.
How much VRAM does Phi-4-reasoning-plus 14B need?
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Phi-4-reasoning-plus 14B?
The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.
What speed will Phi-4-reasoning-plus 14B run at on RTX A4000 16GB?
On RTX A4000 16GB, Phi-4-reasoning-plus 14B achieves approximately 35.0 tokens per second decode speed with a time-to-first-token of 5536ms using Q4_K_M quantization.
Can RTX A4000 16GB run Phi-4-reasoning-plus 14B for coding?
For coding workloads, Phi-4-reasoning-plus 14B on RTX A4000 16GB receives a S grade with 35.0 tok/s and 22K context.
What context window can Phi-4-reasoning-plus 14B use on RTX A4000 16GB?
On RTX A4000 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.
What should I upgrade first if Phi-4-reasoning-plus 14B feels slow on RTX A4000 16GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/phi-4-reasoning-plus-14b-on-a4000-16gb" 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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