MacBook Pro M4 32GBBudget pick
32 GB Unified (+16)120 GB/s (+20)
B
Adds memory headroom for longer context windows and future model growth.32.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Phi 3.5 Mini 4B needs ~10.9 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
Tight fit
Decode
26.6 tok/s
TTFT
7267 ms
Safe context
18K
Memory
10.9 GB / 11.5 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 26.6 tok/s | 3964 ms | 18K |
| Coding | B | Tight fit | 26.6 tok/s | 7267 ms | 18K |
| Agentic Coding | F | Too heavy | 16.1 tok/s | 17490 ms | 18K |
| Reasoning | B | Tight fit | 26.6 tok/s | 8589 ms | 18K |
| RAG | F | Too heavy | 16.1 tok/s | 21862 ms | 18K |
How Phi 3.5 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 | B64 |
Q3_K_S | 3 | 2.0 GB | Low | B64 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Yes, MacBook Air M2 16GB can run Phi 3.5 Mini 4B with a B grade (Tight fit). Expected decode speed: 26.6 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M2 16GB, Phi 3.5 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 3.5 Mini 4B on MacBook Air M2 16GB receives a B grade with 26.6 tok/s and 18K context.
On MacBook Air M2 16GB, Phi 3.5 Mini 4B can safely use up to 18K 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-3.5-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 |
| B64 |
Q4_K_M | 4 | 2.4 GB | Medium | B65 |
Q5_K_M | 5 | 2.9 GB | High | B65 |
Q6_K | 6 | 3.3 GB | High | B66 |
Q8_0 | 8 | 4.3 GB | Very High | B67 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B67 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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.