text model · Qwen2.5-VL · macOS
Can I run Qwen2.5-VL 7B on Apple M1 (8GB)?
No. Qwen2.5-VL 7B needs ~7.1 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
Needs ~7.1 GB even at Q4_K_M, but only ~5.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 1.6 GB: Qwen2.5-VL 7B needs roughly 7.1 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 GB usable for a model. The lightest tracked hardware that runs Qwen2.5-VL 7B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Qwen2.5-VL 7B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~7.1 GB
- Usable on device
- ~5.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 8.29B
- Q4_K_M size
- 5.62 GB
- Q8_0 size
- 9.6 GB
- Context
- 32k
- Memory
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
What you can run instead
Run Qwen2.5-VL 7B on other hardware
FAQ
Can Apple M1 (8GB) run Qwen2.5-VL 7B?
No. Qwen2.5-VL 7B needs ~7.1 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
How much memory does Qwen2.5-VL 7B need?
Apple M1 (8GB) does not have enough memory. At Q4_K_M the weights are ~5.62 GB; with KV cache and runtime overhead, budget ~7.1 GB at a 4k context.
What is the best tool to run Qwen2.5-VL 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-vl-7b/apple-m1-8gb) 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.