Phi 3 Mini 3.8B needs ~16.0 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~34 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
34.3 tok/s
TTFT
5646 ms
Safe context
98K
Memory
16.0 GB / 46.1 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 | 34.3 tok/s | 3079 ms | 98K |
| Coding | B | Runs well | 34.3 tok/s | 5646 ms | 98K |
| Agentic Coding | B | Runs well | 34.3 tok/s | 8212 ms | 98K |
| Reasoning | B | Runs well | 34.3 tok/s | 6672 ms | 98K |
| RAG | B | Runs well | 34.3 tok/s | 10265 ms | 98K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B58 |
Q3_K_S | 3 | 1.9 GB | Low | B58 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniUpgrade options
Raises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$3,199 MSRP
Raises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 34.3 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 16.0 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 Mac mini M4 64GB, Phi 3 Mini 3.8B achieves approximately 34.3 tokens per second decode speed with a time-to-first-token of 5646ms using Q4_K_M quantization.
For coding workloads, Phi 3 Mini 3.8B on Mac mini M4 64GB receives a B grade with 34.3 tok/s and 98K context.
On Mac mini M4 64GB, Phi 3 Mini 3.8B can safely use up to 98K 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-mini-3.8b-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.1 GB |
| Medium |
| B58 |
Q4_K_M | 4 | 2.3 GB | Medium | B58 |
Q5_K_M | 5 | 2.7 GB | High | B59 |
Q6_K | 6 | 3.1 GB | High | B59 |
Q8_0 | 8 | 4.1 GB | Very High | B59 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B60 |
Not always. Mac mini M4 64GB 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.