MacBook Pro M4 Pro 24GBBudget pick
24 GB Unified (+8)273 GB/s (+49)
A
This setup is broadly balanced for this model.56 tok/s decode
~$1,999 MSRP
Phi 4 Mini 4B needs ~6.4 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~53 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
Runs well
Decode
53.3 tok/s
TTFT
3633 ms
Safe context
121K
Memory
6.4 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 | B | Runs well | 53.3 tok/s | 1982 ms | 121K |
| Coding | B | Runs well | 53.3 tok/s | 3633 ms | 121K |
| Agentic Coding | A | Runs well | 53.3 tok/s | 5284 ms | 121K |
| Reasoning | B | Runs well | 53.3 tok/s | 4293 ms | 121K |
| RAG | A | Runs well | 53.3 tok/s | 6605 ms | 121K |
How Phi 4 Mini 4B (4B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B66 |
Q3_K_S | 3 | 2.0 GB | Low | B67 |
NVFP4 | 4 |
Copy-paste commands to run Phi 4 Mini 4B on your machine.
Run
ollama run phi4-miniUpgrade options
Yes, Intel Arc Pro B50 16GB can run Phi 4 Mini 4B with a B grade (Runs well). Expected decode speed: 53.3 tok/s.
Phi 4 Mini 4B (4B parameters) requires approximately 6.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 4 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B50 16GB, Phi 4 Mini 4B achieves approximately 53.3 tokens per second decode speed with a time-to-first-token of 3633ms using Q4_K_M quantization.
For coding workloads, Phi 4 Mini 4B on Intel Arc Pro B50 16GB receives a B grade with 53.3 tok/s and 121K context.
On Intel Arc Pro B50 16GB, Phi 4 Mini 4B can safely use up to 121K tokens of context. The model's official context limit is 128K, 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/phi-4-mini-4b-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:
| Medium |
| B67 |
Q4_K_M | 4 | 2.4 GB | Medium | B67 |
Q5_K_M | 5 | 2.9 GB | High | B67 |
Q6_K | 6 | 3.3 GB | High | B68 |
Q8_0 | 8 | 4.3 GB | Very High | B69 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | A72 |
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.