Qwen 3 4B needs ~7.3 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~56 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
56.0 tok/s
TTFT
3457 ms
Safe context
33K
Memory
7.3 GB / 11.5 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 | A | Runs well | 56.0 tok/s | 1886 ms | 33K |
| Coding | S | Runs well | 56.0 tok/s | 3457 ms | 33K |
| Agentic Coding | A | Tight fit | 56.0 tok/s | 5029 ms | 33K |
| Reasoning | S | Runs well | 56.0 tok/s | 4086 ms | 33K |
| RAG | A | Tight fit | 56.0 tok/s | 6286 ms | 33K |
How Qwen 3 4B (4B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | A79 |
Q3_K_S | 3 | 2.0 GB | Low | A80 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3 4B on your machine.
Run
ollama run qwen3:4bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 27.4 tok/s | |
| Qwen 3 14B | 14B | A | 13.8 tok/s |
Yes, MacBook Pro M2 Pro 16GB can run Qwen 3 4B with a S grade (Runs well). Expected decode speed: 56.0 tok/s.
Qwen 3 4B (4B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 4B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, Qwen 3 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, Qwen 3 4B on MacBook Pro M2 Pro 16GB receives a S grade with 56.0 tok/s and 33K context.
On MacBook Pro M2 Pro 16GB, Qwen 3 4B can safely use up to 33K 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/qwen-3-4b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
2.2 GB |
| Medium |
| A80 |
Q4_K_M | 4 | 2.4 GB | Medium | A80 |
Q5_K_M | 5 | 2.9 GB | High | A81 |
Q6_K | 6 | 3.3 GB | High | A82 |
Q8_0 | 8 | 4.3 GB | Very High | A83 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | A83 |
| Qwen 3 8B | 8B | S | 30.8 tok/s |
 Nemotron Nano 8B | 8B | S | 30.8 tok/s |
 Ministral 3 14B | 14B | B | 13.7 tok/s |
Not always. MacBook Pro M2 Pro 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.