Raises estimated decode speed by about 76%.
~$599 MSRP
![]() |
VOOZH | about |
Phi 3 Medium 14B needs ~15.9 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 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
17.6 tok/s
TTFT
10986 ms
Safe context
53K
Memory
15.9 GB / 23.0 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 | 16.4 tok/s | 6442 ms | 53K |
| Coding | B | Runs well | 16.4 tok/s | 11810 ms | 53K |
| Agentic Coding | B | Tight fit | 16.4 tok/s | 17178 ms | 53K |
| Reasoning | B | Runs well | 16.4 tok/s | 13957 ms | 53K |
| RAG | B | Tight fit | 16.4 tok/s | 21472 ms | 53K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B57 |
Q3_K_S | 3 | 6.9 GB | Low | B58 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade options
Raises estimated decode speed by about 76%.
~$599 MSRP
Raises estimated decode speed by about 85%.
~$2,499 MSRP
7.8 GB |
| Medium |
| B59 |
Q4_K_M | 4 | 8.5 GB | Medium | B59 |
Q5_K_M | 5 | 10.1 GB | High | B60 |
Q6_K | 6 | 11.5 GB | High | B61 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | B61 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Not always. MacBook Pro M2 Pro 32GB 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.