text model · Qwen2.5-Coder · macOS
Can I run Qwen2.5 Coder 7B on Apple M2 (16GB)?
Yes. Qwen2.5 Coder 7B runs on Apple M2 (16GB) at Q4_K_M (~5.8 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~5.8 GB of ~10.5 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M2 (16GB) leaves ~4.7 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~5.8 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~50 W
- Electricity / 1M tokens
- ~$0.12
- Pays for itself after
- ~3,155M tok
At ~$0.15/kWh and the estimated ~18 tok/s, a million generated tokens costs about $0.12 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,199 Apple M2 (16GB) pays for itself after roughly 3,155 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 qwen2.5-coder:7b llama-cli -hf bartowski/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M lms get bartowski/Qwen2.5-Coder-7B-Instruct-GGUF - Parameters
- 7B
- Q4_K_M size
- 4.36 GB
- Q8_0 size
- 7.54 GB
- Context
- 32k
- Ollama tag
- qwen2.5-coder:7b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Power draw
- ~50 W
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
You could also run
Run Qwen2.5 Coder 7B on other hardware
FAQ
Can Apple M2 (16GB) run Qwen2.5 Coder 7B?
Yes. Qwen2.5 Coder 7B runs on Apple M2 (16GB) at Q4_K_M (~5.8 GB of ~10.5 GB usable).
How much memory does Qwen2.5 Coder 7B need?
Apple M2 (16GB) has room to spare. At Q4_K_M the weights are ~4.36 GB; with KV cache and runtime overhead, budget ~5.8 GB at a 4k context.
What is the best tool to run Qwen2.5 Coder 7B 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/qwen2.5-coder-7b/apple-m2-16gb) Sources
- apple.com/newsroom/2022/06
- apple.com/newsroom/2022/07
- developer.apple.com
- github.com/ml-explore
- github.com/raullenchai
- huggingface.co/bartowski
- huggingface.co/Qwen
- lmstudio.ai
- ollama.com/library/qwen2.5-coder
- ollama.com/library/qwen2.5-coder/tags
- stencel.io
- support.apple.com/en-us/103253
- support.apple.com/en-us/111869
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.