Qwen 3 14B needs ~13.8 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~12 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.5 GB host RAM)
Decode
12.3 tok/s
TTFT
15690 ms
Safe context
10K
Memory
13.8 GB / 13.0 GB
Offload
10%
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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload | 13.8 tok/s | 7626 ms | 10K |
| Coding | A | Runs with offload (needs ~0.5 GB host RAM) | 12.3 tok/s | 15690 ms | 10K |
| Agentic Coding | F | Too heavy | 10.0 tok/s | 28227 ms | 10K |
| Reasoning | A | Runs with offload (needs ~0.5 GB host RAM) | 12.3 tok/s | 18543 ms | 10K |
| RAG | F | Too heavy | 10.0 tok/s | 35283 ms |
How Qwen 3 14B (14B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | S92 |
Q3_K_S | 3 | 6.9 GB | Low | S93 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3 14B on your machine.
Run
ollama run qwen3Yes, MacBook Pro M3 Pro 18GB can run Qwen 3 14B with a A grade (Runs with offload (needs ~0.5 GB host RAM)). Expected decode speed: 12.3 tok/s.
Qwen 3 14B (14B parameters) requires approximately 13.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 14B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, Qwen 3 14B achieves approximately 12.3 tokens per second decode speed with a time-to-first-token of 15690ms using Q4_K_M quantization.
For coding workloads, Qwen 3 14B on MacBook Pro M3 Pro 18GB receives a A grade with 12.3 tok/s and 10K context.
On MacBook Pro M3 Pro 18GB, Qwen 3 14B can safely use up to 10K tokens of context. The model's official context limit is 131K, 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/qwen-3-14b-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 10K |
7.8 GB |
| Medium |
| S92 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | S92 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
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 Pro M3 Pro 18GB 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.