RX 7900 XTX 24GBBudget pick
960 GB/s (+560)
B
Raises estimated decode speed by about 214%.87 tok/s decode
Raises estimated decode speed by about 214%.
~$999 MSRP
Phi 3 Medium 14B needs ~15.9 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~26 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
27.7 tok/s
TTFT
6991 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 | 25.8 tok/s | 4099 ms | 53K |
| Coding | B | Runs well | 25.8 tok/s | 7515 ms | 53K |
| Agentic Coding | B | Tight fit | 25.8 tok/s | 10931 ms | 53K |
| Reasoning | B | Runs well | 25.8 tok/s | 8882 ms | 53K |
| RAG | B | Tight fit | 25.8 tok/s | 13664 ms | 53K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on MacBook Pro M1 Max 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 214%.
~$999 MSRP
RTX 3090 24GBBest valueRaises estimated decode speed by about 192%.
~$1,499 MSRP
Yes, MacBook Pro M1 Max 32GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 25.8 tok/s.
Phi 3 Medium 14B (14B parameters) requires approximately 15.9 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 M1 Max 32GB, Phi 3 Medium 14B achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7515ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on MacBook Pro M1 Max 32GB receives a B grade with 25.8 tok/s and 53K context.
On MacBook Pro M1 Max 32GB, Phi 3 Medium 14B can safely use up to 53K 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-medium-14b-on-m1-max-32gb" 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 |
| 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 M1 Max 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.