Phi-4-reasoning-plus 14B needs ~14.5 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~15 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
14.5 tok/s
TTFT
13351 ms
Safe context
24K
Memory
14.5 GB / 16.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 14.5 tok/s | 7282 ms | 24K |
| Coding | S | Tight fit | 14.5 tok/s | 13351 ms | 24K |
| Agentic Coding | A | Very compromised (needs ~0.8 GB host RAM) | 9.2 tok/s | 30739 ms | 24K |
| Reasoning | S | Tight fit | 14.5 tok/s | 15778 ms | 24K |
| RAG | A | Very compromised (needs ~0.8 GB host RAM) | 9.2 tok/s | 38424 ms | 24K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on Intel Arc Pro B50 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, Intel Arc Pro B50 16GB can run Phi-4-reasoning-plus 14B with a S grade (Tight fit). Expected decode speed: 14.5 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.5 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 Intel Arc Pro B50 16GB, Phi-4-reasoning-plus 14B achieves approximately 14.5 tokens per second decode speed with a time-to-first-token of 13351ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on Intel Arc Pro B50 16GB receives a S grade with 14.5 tok/s and 24K context.
On Intel Arc Pro B50 16GB, Phi-4-reasoning-plus 14B can safely use up to 24K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-reasoning-plus-14b-on-arc-pro-b50-16gb" 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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