GLM-4.7 9B — VRAM, Speed & Local Setup

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

Written by Jakub Rusinowski · Last updated February 14, 2026

Model library → GLM-4.7 / GLM-Z1 → GLM-4.7 9B

Compact GLM-4.7 at 9B parameters. Excellent for Chinese-English bilingual tasks and general reasoning. Runs well on 8 GB VRAM.

GLM-4.7 9B needs about 6 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters	9 Billion
Context window	128,000
Architecture	Dense
Provider	Zhipu AI (Z.ai)
Licence	Apache 2.0
Specified at	Q4_K_M
System RAM	16 GB
Record updated	2026-02-14

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	3.0 GB	3.8 GB	~144 tok/s (est.)	Fits comfortably
Q3_K_M	3.8 GB	4.6 GB	~123 tok/s (est.)	Fits comfortably
Q4_K_M	5.4 GB	6.2 GB	~98 tok/s (est.)	Fits comfortably
Q5_K_M	6.4 GB	7.2 GB	~87 tok/s (est.)	Fits comfortably
Q6_K	7.4 GB	8.2 GB	~78 tok/s (est.)	Fits comfortably
Q8_0	9.6 GB	10.4 GB	~64 tok/s (est.)	Fits comfortably
F16	18.0 GB	18.8 GB	~37 tok/s (est.)	Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the GLM-4.7 9B VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs GLM-4.7 9B is the Intel Arc B570 (10 GB).

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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 GLM-4.7 9B

Install Ollama, then run:

ollama run glm4:9b

Weights on Hugging Face: THUDM/glm-4-9b-chat.

Best for: chat, multilingual, reasoning, chinese.

Can I Run GLM-4.7 9B on My GPU?

Other GLM-4.7 / GLM-Z1 Sizes

GLM-Z1 32B (Reasoning) — about 20 GB VRAM at Q4_K_M
GLM-4.7-Flash 30B-A3B — about 19 GB VRAM at Q4_K_M

GLM-4.7 9B — Frequently Asked Questions

How much VRAM does GLM-4.7 9B need?

About 6 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does GLM-4.7 9B run on an RTX 4090 (24 GB)?

Yes. GLM-4.7 9B needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 98 tokens/sec.

How do I run GLM-4.7 9B locally?

Install Ollama and run `ollama run glm4:9b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does GLM-4.7 / GLM-Z1 come in?

GLM-4.7 9B (6 GB), GLM-Z1 32B (Reasoning) (20 GB), GLM-4.7-Flash 30B-A3B (19 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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