MacBook Pro M3 Pro 18GBBudget pick
18 GB Unified (+2)150 GB/s (+82)
A
Raises estimated decode speed by about 168%.48.2 tok/s decode
Raises estimated decode speed by about 168%.
~$1,999 MSRP
Phi 4 Mini 4B needs ~6.5 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~18 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
18.0 tok/s
TTFT
10770 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 | 18.0 tok/s | 5875 ms | 70K |
| Coding | B | Runs well | 18.0 tok/s | 10770 ms | 70K |
| Agentic Coding | A | Runs well | 18.0 tok/s | 15666 ms | 70K |
| Reasoning | B | Runs well | 18.0 tok/s | 12728 ms | 70K |
| RAG | A | Runs well | 18.0 tok/s | 19582 ms | 70K |
How Phi 4 Mini 4B (4B params) fits at each quantization level on MacBook Air M1 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 | 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 |
Copy-paste commands to run Phi 4 Mini 4B on your machine.
Run
ollama run phi4-miniUpgrade options
Raises estimated decode speed by about 168%.
~$1,999 MSRP
Raises estimated decode speed by about 211%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Air M1 16GB can run Phi 4 Mini 4B with a B grade (Runs well). Expected decode speed: 18.0 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 M1 16GB, Phi 4 Mini 4B achieves approximately 18.0 tokens per second decode speed with a time-to-first-token of 10770ms using Q4_K_M quantization.
For coding workloads, Phi 4 Mini 4B on MacBook Air M1 16GB receives a B grade with 18.0 tok/s and 70K context.
On MacBook Air M1 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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-mini-4b-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: