text model · Qwen3 · macOS
Can I run Qwen3 14B on Apple M3 Pro (18GB)?
Yes. Qwen3 14B runs on Apple M3 Pro (18GB) at Q4_K_M (~10.7 GB of ~12 GB usable).
Runs at Q4_K_M using ~10.7 GB of ~12 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M3 Pro (18GB) leaves ~1.3 GB of headroom.
- Q4_K_M needed
- ~10.7 GB
- Usable on device
- ~12 GB
- Device memory
- 18 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~70 W
- Electricity / 1M tokens
- ~$0.32
- Pays for itself after
- ~7,217M tok
At ~$0.15/kWh and the estimated ~9 tok/s, a million generated tokens costs about $0.32 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,299 Apple M3 Pro (18GB) pays for itself after roughly 7,217 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run qwen3:14b llama-cli -hf Qwen/Qwen3-14B-GGUF:Q4_K_M lms get Qwen/Qwen3-14B-GGUF - Parameters
- 14B
- Q4_K_M size
- 9 GB
- Q8_0 size
- 15.7 GB
- Context
- 32k
- Ollama tag
- qwen3:14b
- Memory
- 18 GB unified
- Usable for weights
- ~12 GB
- Power draw
- ~70 W
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
You could also run
Run Qwen3 14B on other hardware
FAQ
Can Apple M3 Pro (18GB) run Qwen3 14B?
Yes. Qwen3 14B runs on Apple M3 Pro (18GB) at Q4_K_M (~10.7 GB of ~12 GB usable).
How much memory does Qwen3 14B need?
Apple M3 Pro (18GB) has room to spare. At Q4_K_M the weights are ~9 GB; with KV cache and runtime overhead, budget ~10.7 GB at a 4k context.
What is the best tool to run Qwen3 14B on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/qwen3-14b/apple-m3-18gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.