GLM-4.6V — Local AI Model by Zhipu AI (Z.ai)

Written by Jakub Rusinowski · Last updated September 19, 2026

Z.ai's vision-language line. The Flash member is the 9B efficiency variant, sharing GLM-4.6V's multimodal capabilities at a fraction of the compute and small enough for an 8 GB card.

Licence

LicenceWhat it permitsApplies to
MITCommercial use permitted
Commercial use permitted. No usage restrictions beyond attribution.		GLM-4.6V-Flash 9B

Hardware Requirements

GLM-4.6V-Flash 9BMin 6 GB VRAM · Q4_K_M · 65,536 ctx ·

Recommended GPU

The cheapest GPU that runs GLM-4.6V locally (min 6 GB VRAM) is the Intel Arc B570 (10 GB).

Affiliate disclosure: Some links on this page are affiliate links — if you buy through them, LLM Configurator may earn a commission at no extra cost to you. As an Amazon Associate, LLM Configurator earns from qualifying purchases.
Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
Check price on Amazon

How to Run Locally

Install Ollama then run: ollama run glm-4-6v

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

GLM-4.6V — Frequently Asked Questions

How much VRAM does GLM-4.6V need?

GLM-4.6V needs about 6 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: GLM-4.6V-Flash 9B (6 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run GLM-4.6V on an RTX 4090 (24 GB)?

Yes — GLM-4.6V 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-4.6V?

Q4_K_M is the best balance of quality and VRAM for GLM-4.6V 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-4.6V with Ollama?

GLM-4.6V has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.