MacBook Air M4 24GBBudget pick
24 GB Unified (+6)
A
Adds memory headroom for longer context windows and future model growth.34.3 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Phi 3 Mini 3.8B needs ~11.0 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~47 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
47.2 tok/s
TTFT
4098 ms
Safe context
21K
Memory
11.0 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 | A | Runs well | 47.2 tok/s | 2235 ms | 21K |
| Coding | B | Tight fit | 47.2 tok/s | 4098 ms | 21K |
| Agentic Coding | F | Too heavy | 32.5 tok/s | 8652 ms | 21K |
| Reasoning | B | Tight fit | 47.2 tok/s | 4843 ms | 21K |
| RAG | F | Too heavy | 32.5 tok/s | 10815 ms | 21K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B64 |
Q3_K_S | 3 | 1.9 GB | Low | B64 |
NVFP4 | 4 | 2.1 GB | Medium | B65 |
Q4_K_M | 4 | 2.3 GB | Medium | B65 |
Q5_K_M | 5 | 2.7 GB | High | B65 |
Q6_K | 6 | 3.1 GB | High | B66 |
Q8_0 | 8 | 4.1 GB | Very High | B67 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B69 |
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniUpgrade 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 Mini 3.8B with a B grade (Tight fit). Expected decode speed: 47.2 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 11.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, Phi 3 Mini 3.8B achieves approximately 47.2 tokens per second decode speed with a time-to-first-token of 4098ms using Q4_K_M quantization.
For coding workloads, Phi 3 Mini 3.8B on MacBook Pro M3 Pro 18GB receives a B grade with 47.2 tok/s and 21K context.
On MacBook Pro M3 Pro 18GB, Phi 3 Mini 3.8B can safely use up to 21K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3-mini-3.8b-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: