Phi-4-reasoning-plus 14B needs ~14.8 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~52 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
51.8 tok/s
TTFT
3739 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 51.8 tok/s | 2040 ms | 22K |
| Coding | S | Tight fit | 51.8 tok/s | 3739 ms | 22K |
| Agentic Coding | A | Very compromised (needs ~0.9 GB host RAM) | 29.6 tok/s | 9506 ms | 22K |
| Reasoning | S | Tight fit | 51.8 tok/s | 4419 ms | 22K |
| RAG | A | Very compromised (needs ~0.9 GB host RAM) | 29.6 tok/s | 11882 ms | 22K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on Tesla P100 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, Tesla P100 16GB can run Phi-4-reasoning-plus 14B with a S grade (Tight fit). Expected decode speed: 51.8 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 Tesla P100 16GB, Phi-4-reasoning-plus 14B achieves approximately 51.8 tokens per second decode speed with a time-to-first-token of 3739ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on Tesla P100 16GB receives a S grade with 51.8 tok/s and 22K context.
On Tesla P100 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-tesla-p100-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: