Phi 3.5 Mini 4B needs ~10.9 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~17 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
16.7 tok/s
TTFT
11578 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 | 16.7 tok/s | 6315 ms | 18K |
| Coding | B | Tight fit | 16.7 tok/s | 11578 ms | 18K |
| Agentic Coding | F | Too heavy | 10.1 tok/s | 27864 ms | 18K |
| Reasoning | B | Tight fit | 16.7 tok/s | 13683 ms | 18K |
| RAG | F | Too heavy | 10.1 tok/s | 34830 ms | 18K |
How Phi 3.5 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 | 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
Raises estimated decode speed by about 95%.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Raises estimated decode speed by about 95%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 67%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Yes, MacBook Air M1 16GB can run Phi 3.5 Mini 4B with a B grade (Tight fit). Expected decode speed: 16.7 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 M1 16GB, Phi 3.5 Mini 4B achieves approximately 16.7 tokens per second decode speed with a time-to-first-token of 11578ms using Q4_K_M quantization.
For coding workloads, Phi 3.5 Mini 4B on MacBook Air M1 16GB receives a B grade with 16.7 tok/s and 18K context.
On MacBook Air M1 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-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:
2.2 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 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.