Qwen3-Coder 30B A3B Instruct needs ~24.9 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~15 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 with offload
Decode
16.6 tok/s
TTFT
11692 ms
Safe context
28K
Memory
24.9 GB / 25.9 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Tight fit | 16.6 tok/s | 6378 ms | 28K |
| Coding | S | Runs with offload | 15.2 tok/s | 12716 ms | 28K |
| Agentic Coding | S | Runs with offload (needs ~0.3 GB host RAM) | 16.0 tok/s | 17629 ms | 28K |
| Reasoning | S | Runs with offload | 16.6 tok/s | 13818 ms | 28K |
| RAG | S | Runs with offload (needs ~0.3 GB host RAM) | 16.0 tok/s | 22037 ms |
How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.9 GB | Low | S93 |
Q3_K_S | 3 | 14.9 GB | Low | S93 |
NVFP4 | 4 |
Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.
Run
ollama run qwen3-coderYes, MacBook Pro M3 Pro 36GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs with offload). Expected decode speed: 15.2 tok/s.
Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 24.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, Qwen3-Coder 30B A3B Instruct achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12716ms using Q4_K_M quantization.
For coding workloads, Qwen3-Coder 30B A3B Instruct on MacBook Pro M3 Pro 36GB receives a S grade with 15.2 tok/s and 28K context.
On MacBook Pro M3 Pro 36GB, Qwen3-Coder 30B A3B Instruct can safely use up to 28K tokens of context. The model's official context limit is 256K, 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-coder-30b-a3b-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 28K |
17.1 GB |
| Medium |
| S93 |
Q4_K_MBest for your GPU | 4 | 18.6 GB | Medium | S92 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M3 Pro 36GB 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.