How much VRAM does GLM-4-9B-0414-abliterated need?

Q6_K is exactly 8,266,642,752 bytes (7.70 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

huihui-ai · text

GLM-4-9B-0414-abliterated

huihui-ai/GLM-4-9B-0414-abliterated

GLM-4-9B-0414-abliterated at Q6_K is exactly 8,266,642,752 bytes (7.70 GiB / 8.27 GB) — an effective 7.035 bits per weight, not the nominal 6.

From the file· summed from 1 file(s)

Parameters

9.4B

Architecture

glm4

Context

—

native (config.json)

License

mit

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q6_K	7.70 GiB	8,266,642,752	7.035	—	jfiekdjdk

Compare with

same modality, comparable size

vs Qwen3-Coder-30B-A3B-Instruct vs Qwen3.6-27B vs Qwen3.8-27B vs gemma-4-12B-it-qat-q4_0-unquantized vs gemma-4-E4B-it vs Qwen3-30B-A3B-Thinking-2507 All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q6_K at roughly 4.92 GiB. The real file is 7.70 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

Architecture unavailable — this repository is gated and no ungated mirror was found. Exact file sizes above are still authoritative; only the KV math needs the config.

Questions people ask

How much VRAM does GLM-4-9B-0414-abliterated need?: Q6_K is exactly 8,266,642,752 bytes (7.70 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of GLM-4-9B-0414-abliterated should I use?: Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.