Phi 4 reasoning vision 15B needs ~13.4 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~13 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
13.2 tok/s
TTFT
14645 ms
Safe context
40K
Memory
13.4 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 | C | Runs well | 13.2 tok/s | 7988 ms | 40K |
| Coding | C | Tight fit | 13.2 tok/s | 14645 ms | 40K |
| Agentic Coding | C | Tight fit | 13.2 tok/s | 21302 ms | 40K |
| Reasoning | C | Tight fit | 13.2 tok/s | 17308 ms | 40K |
| RAG | C | Tight fit | 13.2 tok/s | 26627 ms | 40K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C50 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run Phi 4 reasoning vision 15B on your machine.
Run
lms load hf-jamesburton--phi-4-reasoning-vision-15b-gguf && lms server startUpgrade options
Intel Arc Pro B60 24GBBudget pickRaises estimated decode speed by about 104%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Raises estimated decode speed by about 298%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Yes, Intel Arc Pro B50 16GB can run Phi 4 reasoning vision 15B with a C grade (Tight fit). Expected decode speed: 13.2 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 4 reasoning vision 15B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B50 16GB, Phi 4 reasoning vision 15B achieves approximately 13.2 tokens per second decode speed with a time-to-first-token of 14645ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on Intel Arc Pro B50 16GB receives a C grade with 13.2 tok/s and 40K context.
On Intel Arc Pro B50 16GB, Phi 4 reasoning vision 15B can safely use up to 40K tokens of context. The model's official context limit is —, 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/hf-jamesburton--phi-4-reasoning-vision-15b-gguf-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>
Preview:
8.4 GB |
| Medium |
| C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
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.