HomeCan I run it?Qwen3-30B-A3BQwen3-30B-A3B on a Mac mini 16GB
No. Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the Mac mini M6 16GB has 12.0 GB usable at a ~4k context.
Can I run Qwen3-30B-A3B on a Mac mini 16GB?
Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the Mac mini M6 16GB has 12.0 GB usable. That figure is 16 GB of unified memory on the spec sheet, of which 12.0 GB is usable for weights and cache, at 153 GB/s memory bandwidth. A Qwen3 Mixture-of-Experts with 30B total weights and about 3B active per token, so it needs 30B-class memory but can decode closer to a small dense model. These are estimates from our formula, not measured benchmarks.
- Quant that fits
- None of Q4, Q8 or FP16 needs 22.3 GB
- Memory needed
- 22.3 GB at Q4_K_M, ~4k context
- Usable unified memory
- 12.0 GB of 16 GB unified memory
- Est. tokens/sec
- n/a does not fit
Memory at each quant
| Quant | Need vs 12.0 GB | Verdict |
|---|---|---|
| Q4_K_M | 22.3 GB | Q4Won’t fit |
| Q8_0 | 38.3 GB | Q8Won’t fit |
| FP16 | 71.2 GB | FP16Won’t fit |
Estimated tokens per second
No speed estimate: Qwen3-30B-A3B does not fit in the 12.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.
Context headroom
Even at a short ~4k context, the weights do not fit. Extending context would only increase the gap.
| Context | KV cache (FP16) | Total at Q4_K_M | Verdict |
|---|---|---|---|
| 4k | 0.4 GB | 22.3 GB | Won’t fit |
| 8k | 0.8 GB | 22.7 GB | Won’t fit |
| 16k | 1.6 GB | 23.5 GB | Won’t fit |
| 32k | 3.2 GB | 25.1 GB | Won’t fit |
| 64k | 6.4 GB | 28.3 GB | Won’t fit |
| 128k | 12.9 GB | 34.8 GB | Won’t fit |
What to do instead
- Run R1 Distill 14B instead (11.3 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
- To hold Qwen3-30B-A3B at Q4, you need about 22.3 GB. The GeForce RTX 3090 has 24.0 GB usable ($1499 list price).
- CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
- See the best GPU for this model family and the Qwen3-30B-A3B model page.
Other hardware for Qwen3-30B-A3B
- Qwen3-30B-A3B on an RTX 3060 12GBWon’t fit
- Qwen3-30B-A3B on an RTX 3070 TiWon’t fit
- Qwen3-30B-A3B on an RTX 3080Won’t fit
- Qwen3-30B-A3B on an RTX 3090Tight
- Qwen3-30B-A3B on an RTX 4060 Ti 16GBWon’t fit
- Qwen3-30B-A3B on an RTX 4070 Ti SuperWon’t fit
Other models on a Mac mini 16GB
- Qwen3-32B on a Mac mini 16GBWon’t fit
- Qwen3.8-27B on a Mac mini 16GBWon’t fit
- Gemma 3 27B on a Mac mini 16GBWon’t fit
- Gemma 4 31B on a Mac mini 16GBWon’t fit
- gpt-oss-20b on a Mac mini 16GBWon’t fit
- Mistral Small 24B on a Mac mini 16GBWon’t fit
Quick answers
- Can I run Qwen3-30B-A3B on a Mac mini 16GB?
- No. Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the Mac mini M6 16GB has 12.0 GB usable.
- How much VRAM does Qwen3-30B-A3B need?
- About 22.3 GB at Q4_K_M, 38.3 GB at Q8_0 and 71.2 GB at FP16, estimated for a ~4k context.
- How fast is Qwen3-30B-A3B on a Mac mini 16GB?
- We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.
- What context length can I use?
- The weights already do not fit, so longer context is not the limiting factor.