Phi 4 Mini 4B needs ~6.5 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~27 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
28.6 tok/s
TTFT
6760 ms
Safe context
70K
Memory
6.5 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 | B | Runs well | 26.6 tok/s | 3964 ms | 70K |
| Coding | A | Runs well | 26.6 tok/s | 7267 ms | 70K |
| Agentic Coding | A | Runs well | 26.6 tok/s | 10571 ms | 70K |
| Reasoning | A | Runs well | 26.6 tok/s | 8589 ms | 70K |
| RAG | A | Runs well | 26.6 tok/s | 13214 ms | 70K |
How Phi 4 Mini 4B (4B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B69 |
Q3_K_S | 3 | 2.0 GB | Low | B69 |
NVFP4 | 4 |
Copy-paste commands to run Phi 4 Mini 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 12.7 tok/s | |
| Qwen 3 14B | 14B | A | 6.4 tok/s |
Yes, MacBook Air M2 16GB can run Phi 4 Mini 4B with a A grade (Runs well). Expected decode speed: 26.6 tok/s.
Phi 4 Mini 4B (4B parameters) requires approximately 6.5 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 MacBook Air M2 16GB, Phi 4 Mini 4B achieves approximately 26.6 tokens per second decode speed with a time-to-first-token of 7267ms using Q4_K_M quantization.
For coding workloads, Phi 4 Mini 4B on MacBook Air M2 16GB receives a A grade with 26.6 tok/s and 70K context.
On MacBook Air M2 16GB, Phi 4 Mini 4B can safely use up to 70K 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-m2-air-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.2 GB |
| Medium |
| B69 |
Q4_K_M | 4 | 2.4 GB | Medium | B70 |
Q5_K_M | 5 | 2.9 GB | High | A70 |
Q6_K | 6 | 3.3 GB | High | A71 |
Q8_0 | 8 | 4.3 GB | Very High | A72 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | A72 |
| Qwen 3 8B | 8B | S | 14.3 tok/s |
 Nemotron Nano 8B | 8B | A | 14.3 tok/s |
 Ministral 3 14B | 14B | B | 6.4 tok/s |
Not always. MacBook Air M2 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.