RTX 5090 32GBBudget pick1792 GB/s (+1382)
B
Raises estimated decode speed by about 351%.146.6 tok/s decode
Raises estimated decode speed by about 351%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Phi 3 Medium 14B needs ~16.4 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~33 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
32.5 tok/s
TTFT
5954 ms
Safe context
66K
Memory
16.4 GB / 25.9 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 | 32.5 tok/s | 3248 ms | 66K |
| Coding | B | Runs well | 32.5 tok/s | 5954 ms | 66K |
| Agentic Coding | B | Runs well | 32.5 tok/s | 8661 ms | 66K |
| Reasoning | B | Runs well | 32.5 tok/s | 7037 ms | 66K |
| RAG | B | Runs well | 32.5 tok/s | 10826 ms | 66K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B56 |
Q3_K_S | 3 | 6.9 GB | Low | B57 |
NVFP4 | 4 | 7.8 GB | Medium | B57 |
Q4_K_M | 4 | 8.5 GB | Medium | B58 |
Q5_K_M | 5 | 10.1 GB | High | B59 |
Q6_K | 6 | 11.5 GB | High | B60 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | B61 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 351%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 191%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 Max 36GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 32.5 tok/s.
Phi 3 Medium 14B (14B parameters) requires approximately 16.4 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 M4 Max 36GB, Phi 3 Medium 14B achieves approximately 32.5 tokens per second decode speed with a time-to-first-token of 5954ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on MacBook Pro M4 Max 36GB receives a B grade with 32.5 tok/s and 66K context.
On MacBook Pro M4 Max 36GB, Phi 3 Medium 14B can safely use up to 66K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 Max 36GB 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-m4-max-36gb" 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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