Can I Run GLM-4.7 / GLM-Z1 on MacBook Pro 16" (M5 Max, 128 GB)?

Written by Jakub Rusinowski · Last updated August 15, 2026

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

GLM-4.7 / GLM-Z1 on MacBook Pro 16" (M5 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	66.6 GB	✓ Yes	64K	~5.1 tok/s	64 GB
Q8_0	36.6 GB	✓ Yes	64K	~9.4 tok/s	34 GB
Q6_K	28.9 GB	✓ Yes	64K	~11.9 tok/s	26.2 GB
Q5_K_M	25.3 GB	✓ Yes	64K	~13.7 tok/s	22.7 GB
Q4_K_M	22 GB	✓ Yes	64K	~15.8 tok/s	19.3 GB
Q3_K_M	16.3 GB	✓ Yes	64K	~21.6 tok/s	13.6 GB
Q2_K	13.2 GB	✓ Yes	64K	~27 tok/s	10.5 GB

Which GLM-4.7 / GLM-Z1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GLM-Z1 32B (Reasoning)	22 GB	✓ Fits	~15.8 tok/s
GLM-4.7-Flash 30B-A3B	20.7 GB	✓ Fits	~90.7 tok/s
GLM-4.7 9B	7.4 GB	✓ Fits	~47.8 tok/s

What to watch out for

• This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
• 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 inferred from the parameter count because this model does not publish its full attention configuration.

FAQ

Can I run GLM-4.7 / GLM-Z1 on MacBook Pro 16" (M5 Max, 128 GB)?

Yes, comfortably — GLM-Z1 32B (Reasoning) at Q8_0 needs about 36.6 GB of the 96 GB usable on MacBook Pro 16" (M5 Max, 128 GB), leaving ~59.4 GB spare and running at ~9.4 tok/s (estimated), with room for about 65,536 tokens of context.

Which quantization of GLM-4.7 / GLM-Z1 should I use on MacBook Pro 16" (M5 Max, 128 GB)?

Q8_0 — it needs about 36.6 GB of the 96 GB available, downloads as roughly 34 GB, and runs at an estimated 9.4 tokens/sec with up to 64K of context.

What limits GLM-4.7 / GLM-Z1 on MacBook Pro 16" (M5 Max, 128 GB)?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

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

Other Computers

• GLM-4.7 / GLM-Z1 on MacBook Pro M4 Max 128 GB
• GLM-4.7 / GLM-Z1 on MacBook Pro M4 Max 48 GB
• GLM-4.7 / GLM-Z1 on MacBook Pro M4 Pro 24 GB
• GLM-4.7 / GLM-Z1 on MacBook Air M4 16 GB

Other Models on MacBook Pro 16" (M5 Max, 128 GB)

• GLM-5 / GLM-5.1 on MacBook Pro 16" (M5 Max, 128 GB)
• GLM-6 on MacBook Pro 16" (M5 Max, 128 GB)
• GPT-OSS on MacBook Pro 16" (M5 Max, 128 GB)
• Granite 3.0 on MacBook Pro 16" (M5 Max, 128 GB)
• IBM Granite 4.0 on MacBook Pro 16" (M5 Max, 128 GB)

GLM-4.7 / GLM-Z1 on GPUs

• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5090
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5070
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5060 Ti 8GB
• GLM-4.7 / GLM-Z1 on NVIDIA GeForce RTX 5060

What This Model Is Good At

• Best local LLMs for reasoning
• Best local LLMs for research
• Best local LLMs for mathematics

Model & Tools

• GLM-4.7 / GLM-Z1 model page
• VRAM calculator
• Check your own hardware

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