Qwen 3 30B A3B needs ~24.4 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~18 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
1.4 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1.1 GB host RAM)
Decode
19.0 tok/s
TTFT
10169 ms
Safe context
4K
Memory
24.4 GB / 23.0 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 {ram} 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 | 18.4 tok/s | 5750 ms | 4K |
| Coding | A | Runs with offload | 17.5 tok/s | 11059 ms | 4K |
| Agentic Coding | A | Very compromised | 16.1 tok/s | 17454 ms | 4K |
| Reasoning | A | Runs with offload | 17.5 tok/s | 13070 ms | 4K |
| RAG | A | Very compromised | 16.1 tok/s | 21817 ms | 4K |
How Qwen 3 30B A3B (30.5B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.9 GB | Low | S91 |
Q3_K_S | 3 | 14.9 GB | Low | S90 |
NVFP4Best for your GPU |
Copy-paste commands to run Qwen 3 30B A3B on your machine.
Run
ollama run qwen3:30b-a3bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 35B A3B | 35B | A | 16.6 tok/s |
Yes, MacBook Pro M2 Pro 32GB can run Qwen 3 30B A3B with a A grade (Runs with offload). Expected decode speed: 17.5 tok/s.
Qwen 3 30B A3B (30.5B parameters) requires approximately 24.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 30B A3B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, Qwen 3 30B A3B achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11059ms using Q4_K_M quantization.
For coding workloads, Qwen 3 30B A3B on MacBook Pro M2 Pro 32GB receives a A grade with 17.5 tok/s and 4K context.
On MacBook Pro M2 Pro 32GB, Qwen 3 30B A3B can safely use up to 4K 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-3-30b-a3b-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
17.1 GB |
| Medium |
| S90 |
Q4_K_M | 4 | 18.6 GB | Medium | F0 |
Q5_K_M | 5 | 22.0 GB | High | F0 |
Q6_K | 6 | 25.0 GB | High | F0 |
Q8_0 | 8 | 32.6 GB | Very High | F0 |
F16 | 16 | 62.5 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 M2 Pro 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.