Phi 3 Medium 14B needs ~19.4 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~30 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
30.2 tok/s
TTFT
6408 ms
Safe context
128K
Memory
19.4 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 | 30.2 tok/s | 3495 ms | 128K |
| Coding | B | Runs well | 30.2 tok/s | 6408 ms | 128K |
| Agentic Coding | B | Runs well | 30.2 tok/s | 9321 ms | 128K |
| Reasoning | B | Runs well | 30.2 tok/s | 7573 ms | 128K |
| RAG | B | Runs well | 30.2 tok/s | 11651 ms | 128K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C53 |
Q3_K_S | 3 | 6.9 GB | Low | C53 |
NVFP4 | 4 | 7.8 GB | Medium | C53 |
Q4_K_M | 4 | 8.5 GB | Medium | C54 |
Q5_K_M | 5 | 10.1 GB | High | C54 |
Q6_K | 6 | 11.5 GB | High | C55 |
Q8_0 | 8 | 15.0 GB | Very High | B56 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | B59 |
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade-Optionen
Raises estimated decode speed by about 132%.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Raises estimated decode speed by about 113%.
ca. $3,999 MSRP
Yes, MacBook Pro M3 Max 64GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 30.2 tok/s.
Phi 3 Medium 14B (14B parameters) requires approximately 19.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 64GB, Phi 3 Medium 14B achieves approximately 30.2 tokens per second decode speed with a time-to-first-token of 6408ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on MacBook Pro M3 Max 64GB receives a B grade with 30.2 tok/s and 128K context.
On MacBook Pro M3 Max 64GB, Phi 3 Medium 14B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Max 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3-medium-14b-on-m3-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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