Can GLM-4 9B run on MacBook Pro M2 Pro 16GB?

YES — Runs Great

A74Great

○Estimated from fit model

GLM-4 9B needs ~8.7 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.7 GB, 27.9 tok/s, Runs well

8.7 GB required11.5 GB available

76% VRAM used

Fit status

Runs well

Decode

27.9 tok/s

TTFT

6941 ms

Safe context

89K

Memory

8.7 GB / 11.5 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGLM-4 9B on MacBook Pro M2 Pro 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 27.9 tok/s decode · 6.9s TTFT (warm) · 70 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	27.9 tok/s	3786 ms	89K
Coding	A	Runs well	27.9 tok/s	6941 ms	89K
Agentic Coding	A	Runs well	27.9 tok/s	10096 ms	89K
Reasoning	A	Runs well	27.9 tok/s	8203 ms	89K
RAG	A	Runs well	27.9 tok/s	12620 ms	89K

Quantization options

How GLM-4 9B (9B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A72
Q3_K_S	3	4.4 GB	Low	A73
NVFP4	4	5.0 GB	Medium	A74
Q4_K_M	4	5.5 GB	Medium	A74
Q5_K_M	5	6.5 GB	High	A74
Q6_KBest for your GPU	6	7.4 GB	High	A73
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run GLM-4 9B on your machine.

Run

ollama run glm4

Your hardware

More models your MacBook Pro M2 Pro 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 14B	14B	A	13.8 tok/s
👁 Mistral Ministral 3 14B	14B	B	13.7 tok/s
👁 AllenAI OLMo 2 13B	13B	B	15.7 tok/s
👁 Mistral AI Pixtral 12B	12B	B	18.1 tok/s

Frequently asked questions

See all results for MacBook Pro M2 Pro 16GB See all hardware for GLM-4 9B

URL: https://willitrunai.com/can-run/glm-4-9b-on-m2-pro-16gb

⇱ GLM-4 9B on MacBook Pro M2 Pro 16GB? YES