Phi-4 Mini Reasoning 4B needs ~6.4 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~19 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
18.9 tok/s
TTFT
10232 ms
Safe context
72K
Memory
6.4 GB / 11.5 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 | 18.9 tok/s | 5581 ms | 72K |
| Coding | S | Runs well | 18.9 tok/s | 10232 ms | 72K |
| Agentic Coding | S | Runs well | 18.9 tok/s | 14883 ms | 72K |
| Reasoning | S | Runs well | 18.9 tok/s | 12092 ms | 72K |
| RAG | S | Runs well | 18.9 tok/s | 18603 ms | 72K |
How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | A85 |
Q3_K_S | 3 | 1.9 GB | Low | S85 |
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 | 8 tok/s | |
| Qwen 3 14B | 14B | B | 4 tok/s |
Yes, MacBook Air M1 16GB can run Phi-4 Mini Reasoning 4B with a S grade (Runs well). Expected decode speed: 18.9 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 6.4 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 Air M1 16GB, Phi-4 Mini Reasoning 4B achieves approximately 18.9 tokens per second decode speed with a time-to-first-token of 10232ms using Q4_K_M quantization.
For coding workloads, Phi-4 Mini Reasoning 4B on MacBook Air M1 16GB receives a S grade with 18.9 tok/s and 72K context.
On MacBook Air M1 16GB, Phi-4 Mini Reasoning 4B can safely use up to 72K 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-m1-16gb" 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 |
| S86 |
Q4_K_M | 4 | 2.3 GB | Medium | S86 |
Q5_K_M | 5 | 2.7 GB | High | S86 |
Q6_K | 6 | 3.1 GB | High | S87 |
Q8_0 | 8 | 4.1 GB | Very High | S88 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | S89 |
| Qwen 3.5 4B | 4B | S | 18 tok/s |
| Qwen 3 8B | 8B | S | 9 tok/s |
Not always. MacBook Air M1 16GB 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.