Phi-4-reasoning-plus 14B needs ~15.5 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~7 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
Tight fit
Decode
7.8 tok/s
TTFT
24845 ms
Safe context
25K
Memory
15.5 GB / 17.3 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 7.2 tok/s | 14568 ms | 25K |
| Coding | S | Tight fit | 7.2 tok/s | 26708 ms | 25K |
| Agentic Coding | A | Runs with offload | 6.4 tok/s | 44040 ms | 25K |
| Reasoning | S | Tight fit | 7.2 tok/s | 31564 ms | 25K |
| RAG | A | Runs with offload | 6.4 tok/s | 55051 ms | 25K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | S88 |
Q3_K_S | 3 | 7.2 GB | Low | S90 |
NVFP4 | 4 |
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoningYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Magistral Small 2507 | 24B | B | 3.7 tok/s | |
| Devstral Small 2 24B Instruct | 24B | B | 3.7 tok/s |
Yes, Mac mini M2 24GB can run Phi-4-reasoning-plus 14B with a S grade (Tight fit). Expected decode speed: 7.2 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 15.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M2 24GB, Phi-4-reasoning-plus 14B achieves approximately 7.2 tokens per second decode speed with a time-to-first-token of 26708ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on Mac mini M2 24GB receives a S grade with 7.2 tok/s and 25K context.
On Mac mini M2 24GB, Phi-4-reasoning-plus 14B can safely use up to 25K tokens of context. The model's official context limit is 33K, 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-reasoning-plus-14b-on-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
8.2 GB |
| Medium |
| S91 |
Q4_K_M | 4 | 9.0 GB | Medium | S91 |
Q5_K_M | 5 | 10.6 GB | High | S91 |
Q6_KBest for your GPU | 6 | 12.1 GB | High | S90 |
Q8_0 | 8 | 15.7 GB | Very High | F0 |
F16 | 16 | 30.1 GB | Maximum | F0 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. Mac mini M2 24GB 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.