text model · Qwen2.5-Coder · macOS
Can I run Qwen2.5 Coder 32B on Apple M5 (16GB)?
No. Qwen2.5 Coder 32B needs ~20.7 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
Needs ~20.7 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 10.2 GB: Qwen2.5 Coder 32B needs roughly 20.7 GB at Q4_K_M and Apple M5 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Qwen2.5 Coder 32B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Qwen2.5 Coder 32B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~20.7 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
- Parameters
- 32B
- Q4_K_M size
- 18.49 GB
- Q8_0 size
- 32.43 GB
- Context
- 32k
- Ollama tag
- qwen2.5-coder:32b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
What you can run instead
Run Qwen2.5 Coder 32B on other hardware
FAQ
Can Apple M5 (16GB) run Qwen2.5 Coder 32B?
No. Qwen2.5 Coder 32B needs ~20.7 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
How much memory does Qwen2.5 Coder 32B need?
Apple M5 (16GB) does not have enough memory. At Q4_K_M the weights are ~18.49 GB; with KV cache and runtime overhead, budget ~20.7 GB at a 4k context.
What is the best tool to run Qwen2.5 Coder 32B 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/qwen2.5-coder-32b/apple-m5-16gb) 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.