GLM-4.7 / GLM-Z1 — Local AI Model by Zhipu AI

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated July 10, 2026

Zhipu AI's February 2026 MoE model. GLM-4.7 uses a 32B active MoE architecture and scores on par with Claude Opus 4.5 on hallucination leaderboards. The reasoning-focused GLM-Z1 variant posts near-GPT-5.2 scores on the Artificial Intelligence Index. Strong multilingual support with particular depth in Chinese.

Hardware Requirements

GLM-4.7 9B	Min 6 GB VRAM · Q4_K_M · 128,000 ctx · `ollama run glm4:9b`
GLM-Z1 32B (Reasoning)	Min 20 GB VRAM · Q4_K_M · 128,000 ctx · `ollama run glm-z1:32b`

Recommended GPU

The cheapest GPU that runs GLM-4.7 / GLM-Z1 locally (min 6 GB VRAM) is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB

Launch MSRP: $219

2026 prices are volatile — check the current listing.

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How to Run Locally

Install Ollama then run: ollama run glm4:9b

Minimum VRAM: 6 GB. For best results use Q4_K_M quantization.

GLM-4.7 / GLM-Z1 — Frequently Asked Questions

How much VRAM does GLM-4.7 / GLM-Z1 need?

GLM-4.7 / GLM-Z1 needs about 6 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: GLM-4.7 9B (6 GB, Q4_K_M); GLM-Z1 32B (Reasoning) (20 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run GLM-4.7 / GLM-Z1 on an RTX 4090 (24 GB)?

Yes — GLM-4.7 / GLM-Z1 runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.

What quantization should I use for GLM-4.7 / GLM-Z1?

Q4_K_M is the best balance of quality and VRAM for GLM-4.7 / GLM-Z1 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run GLM-4.7 / GLM-Z1 with Ollama?

Install Ollama, then run: ollama run glm4:9b. This downloads GLM-4.7 / GLM-Z1 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.