text model · GLM · macOS

Can I run GLM-4.7-Flash on Apple M2 (16GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. GLM-4.7-Flash needs ~19.2 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

Needs ~19.2 GB Device usable ~10.5 GB

Needs ~19.2 GB even at Q4_K_M, but only ~10.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 8.7 GB: GLM-4.7-Flash needs roughly 19.2 GB at Q4_K_M and Apple M2 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs GLM-4.7-Flash is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See GLM-4.7-Flash on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~19.2 GB
Usable on device: ~10.5 GB
Device memory: 16 GB

Share on X Share on Reddit

Which quant fits

Quant ladder vs ~10.5 GB usable

Q2_K

~14.7 GB

Q3_K_M

~16.8 GB

Q4_K_M

~19.2 GB

Q5_K_M

~23.5 GB

Q6_K

~26.7 GB

Q8_0

~31.8 GB

FP16

~57.9 GB

The line marks Apple M2 (16GB)'s ~10.5 GB budget; rungs past it are too large.

Model GLM

Parameters: 30B (MoE, 3B active)
Q4_K_M size: 17.05 GB
Q8_0 size: 29.66 GB
Context: 200k
Ollama tag: glm-4.7-flash:latest

Full GLM-4.7-Flash requirements →

Device macOS

Memory: 16 GB unified
Usable for weights: ~10.5 GB
Power draw: ~50 W
Best runtime: Ollama (llama.cpp Metal backend) / MLX

Best models for Apple M2 (16GB) →

What you can run instead

Run GLM-4.7-Flash 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 (32GB)

FAQ

Can Apple M2 (16GB) run GLM-4.7-Flash?

No. GLM-4.7-Flash needs ~19.2 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

How much memory does GLM-4.7-Flash need?

Apple M2 (16GB) does not have enough memory. At Q4_K_M the weights are ~17.05 GB; with KV cache and runtime overhead, budget ~19.2 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run GLM-4.7-Flash 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.

Markdown

[![GLM-4.7-Flash on Apple M2 (16GB)](https://localmodel.run/badge/glm-4.7-flash/apple-m2-16gb.svg)](https://localmodel.run/can-i-run/glm-4.7-flash/apple-m2-16gb)

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.