Can I Run Qwen 2.5 VL on MacBook Pro 16" (M5 Max, 128 GB)?

Superseded model. Qwen 2.5 VL has been superseded by Qwen 3. This page is kept for reference; the newer family is a better starting point. View Qwen 3 →

Written by Jakub Rusinowski · Last updated January 25, 2025

Yes — comfortably

Yes, comfortably — Qwen 2.5 VL 72B Instruct at Q6_K needs about 63.7 GB of the 96 GB usable on MacBook Pro 16" (M5 Max, 128 GB), leaving ~32.3 GB spare and running at ~5.4 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~5.4 tok/s

MacBook Pro 16" (M5 Max, 128 GB) — what it gives a model

Usable memory for models	96 GB
Memory bandwidth	614 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

Qwen 2.5 VL on MacBook Pro 16" (M5 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	150.3 GB	✗ No	—	—	146.8 GB
Q8_0	81.5 GB	✓ Yes	32K	~4.2 tok/s	78 GB
Q6_K	63.7 GB	✓ Yes	64K	~5.4 tok/s	60.2 GB
Q5_K_M	55.5 GB	✓ Yes	64K	~6.2 tok/s	52 GB
Q4_K_M	47.8 GB	✓ Yes	64K	~7.2 tok/s	44.3 GB
Q3_K_M	34.8 GB	✓ Yes	64K	~10 tok/s	31.3 GB
Q2_K	27.6 GB	✓ Yes	64K	~12.7 tok/s	24.1 GB

Which Qwen 2.5 VL sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen 2.5 VL 72B Instruct	47.8 GB	✓ Fits	~7.2 tok/s
Qwen 2.5 VL 7B Instruct	6.3 GB	✓ Fits	~53.8 tok/s

What to watch out for

Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.

MacBook Pro M5 Max 128 GB limitations

Unified memory is soldered and cannot be upgraded after purchase.

Recommended setup

Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
128 GB unified memory at 614 GB/s, shared between CPU and GPU.
macOS reserves a share of unified memory for the system, so not all of it is available to a model.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

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