MacBook Air M4 24GBBudget pick
24 GB Unified (+6)
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.
~$1,099 MSRP
Phi 3.5 Mini 4B needs ~11.1 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~45 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
44.9 tok/s
TTFT
4314 ms
Safe context
21K
Memory
11.1 GB / 13.0 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 | 44.9 tok/s | 2353 ms | 21K |
| Coding | B | Tight fit | 44.9 tok/s | 4314 ms | 21K |
| Agentic Coding | F | Too heavy | 30.7 tok/s | 9186 ms | 21K |
| Reasoning | B | Tight fit | 44.9 tok/s | 5098 ms | 21K |
| RAG | F | Too heavy | 30.7 tok/s | 11482 ms | 21K |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B63 |
Q3_K_S | 3 | 2.0 GB | Low | B63 |
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.
~$1,099 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 Pro M3 Pro 18GB can run Phi 3.5 Mini 4B with a B grade (Tight fit). Expected decode speed: 44.9 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 11.1 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 Pro M3 Pro 18GB, Phi 3.5 Mini 4B achieves approximately 44.9 tokens per second decode speed with a time-to-first-token of 4314ms using Q4_K_M quantization.
For coding workloads, Phi 3.5 Mini 4B on MacBook Pro M3 Pro 18GB receives a B grade with 44.9 tok/s and 21K context.
On MacBook Pro M3 Pro 18GB, Phi 3.5 Mini 4B can safely use up to 21K 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-m3-pro-18gb" 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 |
| B63 |
Q4_K_M | 4 | 2.4 GB | Medium | B64 |
Q5_K_M | 5 | 2.9 GB | High | B64 |
Q6_K | 6 | 3.3 GB | High | B65 |
Q8_0 | 8 | 4.3 GB | Very High | B66 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B67 |
Not always. MacBook Pro M3 Pro 18GB 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.