text model · Qwen2.5-VL · macOS
Can I run Qwen2.5-VL 3B on Apple M4 Pro (48GB)?
Yes. Qwen2.5-VL 3B runs on Apple M4 Pro (48GB) at Q4_K_M (~4.4 GB of ~32 GB usable).
Runs at Q4_K_M using ~4.4 GB of ~32 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Pro (48GB) leaves ~27.6 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~4.4 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~140 W
- Electricity / 1M tokens
- ~$0.08
- Pays for itself after
- ~5,712M tok
At ~$0.15/kWh and the estimated ~72 tok/s, a million generated tokens costs about $0.08 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$2,399 Apple M4 Pro (48GB) pays for itself after roughly 5,712 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 2 load the same Q4_K_M weights.
llama-cli -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M lms get ggml-org/Qwen2.5-VL-3B-Instruct-GGUF How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 3.75B
- Q4_K_M size
- 3.05 GB
- Q8_0 size
- 5.1 GB
- Context
- 32k
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Power draw
- ~140 W
- Best runtime
- Ollama (MLX backend) / MLX direct
You could also run
Run Qwen2.5-VL 3B on other hardware
FAQ
Can Apple M4 Pro (48GB) run Qwen2.5-VL 3B?
Yes. Qwen2.5-VL 3B runs on Apple M4 Pro (48GB) at Q4_K_M (~4.4 GB of ~32 GB usable).
How much memory does Qwen2.5-VL 3B need?
Apple M4 Pro (48GB) has room to spare. At Q4_K_M the weights are ~3.05 GB; with KV cache and runtime overhead, budget ~4.4 GB at a 4k context.
What is the best tool to run Qwen2.5-VL 3B 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-3b/apple-m4-pro-48gb) Sources
- apple.com/newsroom/2024/10/apple-introduces-m4-pro-and-m4-max
- apple.com/newsroom/2024/10/new-macbook-pro-features-m4-family-of-chips-and-apple-intelligence
- blog.peddals.com
- github.com/ml-explore
- github.com/raullenchai
- huggingface.co/ggml-org
- huggingface.co/Qwen
- lmstudio.ai
- support.apple.com/en-us/103253
- support.apple.com/en-us/121553
- support.apple.com/en-us/121555
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.