Phi-4-reasoning-plus 14B needs ~12.9 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q3_K_S quantization, expect ~8 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
3.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.5 tok/s
TTFT
35028 ms
Safe context
4K
Memory
14.6 GB / 11.5 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~1.1 GB host RAM) | 6.3 tok/s | 16645 ms | 4K |
| Coding | F | Too heavy | 5.5 tok/s | 35028 ms | 4K |
| Agentic Coding | F | Too heavy | 4.4 tok/s | 63497 ms | 4K |
| Reasoning | F | Too heavy | 5.5 tok/s | 41397 ms | 4K |
| RAG | F | Too heavy | 4.4 tok/s | 79372 ms | 4K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | S92 |
Q3_K_S | 3 | 7.2 GB | Low | S92 |
NVFP4Best for your GPU | 4 | 8.2 GB | Medium | S91 |
Q4_K_M | 4 | 9.0 GB | Medium | F0 |
Q5_K_M | 5 | 10.6 GB | High | F0 |
Q6_K | 6 | 12.1 GB | High | F0 |
Q8_0 | 8 | 15.7 GB | Very High | F0 |
F16 | 16 | 30.1 GB | Maximum | F0 |
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoningOpciones de mejora
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$799 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,099 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,099 MSRP
RTX 4090 24GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,599 MSRP
Yes, MacBook Air M2 16GB can run Phi-4-reasoning-plus 14B at Q3_K_S quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 14.6 GB which exceeds available memory, but at Q3_K_S it needs only 12.9 GB. Expected decode speed: 7.5 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.6 GB at Q4_K_M quantization. On MacBook Air M2 16GB, it fits at Q3_K_S using 12.9 GB.
The recommended quantization is Q4_K_M, but on MacBook Air M2 16GB the best fitting quantization is Q3_K_S, which uses 12.9 GB.
On MacBook Air M2 16GB, Phi-4-reasoning-plus 14B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25724ms using Q3_K_S quantization.
For coding workloads, Phi-4-reasoning-plus 14B on MacBook Air M2 16GB receives a F grade with 5.5 tok/s and 4K context.
On MacBook Air M2 16GB, Phi-4-reasoning-plus 14B can safely use up to 9K tokens of context at Q3_K_S quantization. The model's official context limit is 33K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Air M2 16GB 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.
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