text model · Qwen3 · macOS

Can I run Qwen3 32B on Apple M4 Pro (48GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated ~11 tok/s est.

Yes. Qwen3 32B runs on Apple M4 Pro (48GB) at Q4_K_M (~22 GB of ~32 GB usable).

Needs ~22 GB Device usable ~32 GB

Runs at Q4_K_M using ~22 GB of ~32 GB usable.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Pro (48GB) leaves ~10 GB of headroom.

Q4_K_M needed: ~22 GB
Usable on device: ~32 GB
Device memory: 48 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~32 GB usable

Q2_K

~15.6 GB

Q3_K_M

~17.8 GB

Q4_K_M

~22 GB

Q5_K_M

~25 GB

Q6_K

~28.4 GB

Q8_0

~37 GB

FP16

~68.2 GB

The line marks Apple M4 Pro (48GB)'s ~32 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~140 W
Electricity / 1M tokens: ~$0.53

At ~$0.15/kWh and the estimated ~11 tok/s, a million generated tokens costs about $0.53 in electricity. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run qwen3:32b

llama.cpp

$ llama-cli -hf Qwen/Qwen3-32B-GGUF:Q4_K_M

LM Studio

$ lms get Qwen/Qwen3-32B-GGUF

Model Qwen3

Parameters: 32B
Q4_K_M size: 19.8 GB
Q8_0 size: 34.8 GB
Context: 32k
Ollama tag: qwen3:32b

Full Qwen3 32B requirements →

Device macOS

Memory: 48 GB unified
Usable for weights: ~32 GB
Power draw: ~140 W
Best runtime: Ollama (MLX backend) / MLX direct

Best models for Apple M4 Pro (48GB) →

You could also run

Run Qwen3 32B on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M5 Pro (48GB)

FAQ

Can Apple M4 Pro (48GB) run Qwen3 32B?

Yes. Qwen3 32B runs on Apple M4 Pro (48GB) at Q4_K_M (~22 GB of ~32 GB usable).

How much memory does Qwen3 32B need?

Apple M4 Pro (48GB) has room to spare. At Q4_K_M the weights are ~19.8 GB; with KV cache and runtime overhead, budget ~22 GB at a 4k context.

What is the best tool to run Qwen3 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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Markdown

[![Qwen3 32B on Apple M4 Pro (48GB)](https://localmodel.run/badge/qwen3-32b/apple-m4-pro-48gb.svg)](https://localmodel.run/can-i-run/qwen3-32b/apple-m4-pro-48gb)

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.