Phi-4 Mini Reasoning 4B needs ~6.6 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~47 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
50.8 tok/s
TTFT
3812 ms
Safe context
85K
Memory
6.6 GB / 13.0 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 47.2 tok/s | 2235 ms | 85K |
| Coding | S | Runs well | 47.2 tok/s | 4098 ms | 85K |
| Agentic Coding | S | Runs well | 47.2 tok/s | 5961 ms | 85K |
| Reasoning | S | Runs well | 47.2 tok/s | 4843 ms | 85K |
| RAG | S | Runs well | 47.2 tok/s | 7451 ms | 85K |
How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | A84 |
Q3_K_S | 3 | 1.9 GB | Low | A85 |
NVFP4 | 4 |
Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 21.4 tok/s | |
| Qwen 3 14B | 14B | A | 12.3 tok/s |
Yes, MacBook Pro M3 Pro 18GB can run Phi-4 Mini Reasoning 4B with a S grade (Runs well). Expected decode speed: 47.2 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 6.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, Phi-4 Mini Reasoning 4B achieves approximately 47.2 tokens per second decode speed with a time-to-first-token of 4098ms using Q4_K_M quantization.
For coding workloads, Phi-4 Mini Reasoning 4B on MacBook Pro M3 Pro 18GB receives a S grade with 47.2 tok/s and 85K context.
On MacBook Pro M3 Pro 18GB, Phi-4 Mini Reasoning 4B can safely use up to 85K tokens of context. The model's official context limit is 131K, 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-reasoning-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.1 GB |
| Medium |
| A85 |
Q4_K_M | 4 | 2.3 GB | Medium | A85 |
Q5_K_M | 5 | 2.7 GB | High | S85 |
Q6_K | 6 | 3.1 GB | High | S86 |
Q8_0 | 8 | 4.1 GB | Very High | S87 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | S89 |
| Qwen 3.5 4B | 4B | S | 48.2 tok/s |
| Qwen 3 8B | 8B | S | 24.1 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | A | 10.6 tok/s |
Not always. MacBook Pro M3 Pro 18GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.