Phi-4 14B needs ~19.4 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~28 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
16K
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 | A | Runs well | 28.1 tok/s | 3758 ms | 16K |
| Coding | A | Runs well | 28.1 tok/s | 6889 ms | 16K |
| Agentic Coding | A | Runs well | 28.1 tok/s | 10020 ms | 16K |
| Reasoning | A | Runs well | 28.1 tok/s | 8141 ms | 16K |
| RAG | A | Runs well | 28.1 tok/s | 12525 ms | 16K |
How Phi-4 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 | A74 |
Q3_K_S | 3 | 6.9 GB | Low | A74 |
NVFP4 | 4 |
Copy-paste commands to run Phi-4 14B on your machine.
Run
ollama run phi4Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 36.3 tok/s | |
| Qwen 3.5 27B | 27B | S | 15.7 tok/s |
Yes, MacBook Pro M3 Max 64GB can run Phi-4 14B with a A grade (Runs well). Expected decode speed: 28.1 tok/s.
Phi-4 14B (14B parameters) requires approximately 19.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 14B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 64GB, Phi-4 14B achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6889ms using Q4_K_M quantization.
For coding workloads, Phi-4 14B on MacBook Pro M3 Max 64GB receives a A grade with 28.1 tok/s and 16K context.
On MacBook Pro M3 Max 64GB, Phi-4 14B can safely use up to 16K tokens of context. The model's official context limit is 16K, 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-4-14b-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>
Preview:
7.8 GB |
| Medium |
| A74 |
Q4_K_M | 4 | 8.5 GB | Medium | A74 |
Q5_K_M | 5 | 10.1 GB | High | A75 |
Q6_K | 6 | 11.5 GB | High | A75 |
Q8_0 | 8 | 15.0 GB | Very High | A76 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | A80 |
| Qwen 3.6 27B | 27B | S | 12 tok/s |
| Qwen 3.6 35B A3B | 35B | S | 33.5 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 37.5 tok/s |
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.