Phi-4 Mini Reasoning 4B needs ~8.1 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 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
53.2 tok/s
TTFT
3639 ms
Safe context
131K
Memory
8.1 GB / 23.0 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 | A | Runs well | 53.2 tok/s | 1985 ms | 131K |
| Coding | A | Runs well | 53.2 tok/s | 3639 ms | 131K |
| Agentic Coding | S | Runs well | 53.2 tok/s | 5293 ms | 131K |
| Reasoning | A | Runs well | 53.2 tok/s | 4301 ms | 131K |
| RAG | S | Runs well | 53.2 tok/s | 6617 ms | 131K |
How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | A81 |
Q3_K_S | 3 | 1.9 GB | Low | A81 |
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 |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 31.5 tok/s | |
| Qwen 3.5 27B | 27B | S | 14.1 tok/s |
Yes, MacBook Pro M2 Max 32GB can run Phi-4 Mini Reasoning 4B with a A grade (Runs well). Expected decode speed: 53.2 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 8.1 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 M2 Max 32GB, Phi-4 Mini Reasoning 4B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.
For coding workloads, Phi-4 Mini Reasoning 4B on MacBook Pro M2 Max 32GB receives a A grade with 53.2 tok/s and 131K context.
On MacBook Pro M2 Max 32GB, Phi-4 Mini Reasoning 4B can safely use up to 131K 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-m2-max-32gb" 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 |
| A81 |
Q4_K_M | 4 | 2.3 GB | Medium | A81 |
Q5_K_M | 5 | 2.7 GB | High | A82 |
Q6_K | 6 | 3.1 GB | High | A82 |
Q8_0 | 8 | 4.1 GB | Very High | A82 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | A85 |
| Qwen 3.6 27B | 27B | S | 11.6 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 33.3 tok/s |
| Qwen 3.5 9B | 9B | S | 45.4 tok/s |
Not always. MacBook Pro M2 Max 32GB 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.