How much VRAM does GLM-4-9B-Chat need?

About 5.9 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 7.1 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will GLM-4-9B-Chat run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 5.9 GB, which leaves 2.1 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 76 of 78 do.

Which quantization of GLM-4-9B-Chat should I use?

Q8_0 has the lowest published quality loss (0.4%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q8_0, AWQ INT4.

GLM-4-9B-Chat

Zhipu GLM-4

Zhipu GLM-4 open 9B with 128K context, tool calling, and strong bilingual (EN/ZH) performance.

⬇ 6.0K HF downloads♥ 25 likeszai-org/glm-4-9b-chat-hf· stats from 11/11/2026

Consumer GPUMac / Apple Silicon

131K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

+0.4%

Perplexity rise (best build)

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	5.9 GB	3.0%	135 tok/s	Estimated	Calc HF
GGUF	Q8_0	8.5	10.2 GB	0.4%	105 tok/s	Estimated	Calc HF
AWQ	INT4	4	4.9 GB	4.2%	178 tok/s	Estimated	Calc HF

Which file each size is for

A file size is not a memory figure: the VRAM column is the calculator's total at 4K context — weights plus KV cache plus a 10% activation buffer. Units differ on purpose: file sizes are decimal GB, as Hugging Face lists them; the VRAM column is GiB (1024³ bytes) with 2% added to the weights, so 7.12 GB of file counts as about 6.8 GiB there. A vision projector (mmproj), where the repo ships one, is a separate download and is in neither number.

GGUF Q4_K_MSized at the calculator's generic rate for this level; no file was checked.
GGUF Q8_0Sized at the calculator's generic rate for this level; no file was checked.
AWQ INT4Sized at the calculator's generic rate for this level; no file was checked.

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Running GLM-4-9B-Chat locally

At Q4_K_M and 4K of context, GLM-4-9B-Chat needs about 5.9 GB — 5.2 GB of weights, 0.16 GB of KV cache and a 0.5 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 9 of them, from the RTX 4060 up — and 76 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 1.09 GB, taking the total to 7.1 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 11 GB.

Which build to download

This index tracks 2 formats for it — GGUF, AWQ — across 3 levels. Q8_0 carries the lowest published perplexity loss at 0.4%. The fastest level listed is AWQ INT4 at about 178 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does GLM-4-9B-Chat need?: About 5.9 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 7.1 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will GLM-4-9B-Chat run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 5.9 GB, which leaves 2.1 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 76 of 78 do.
Which quantization of GLM-4-9B-Chat should I use?: Q8_0 has the lowest published quality loss (0.4%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q8_0, AWQ INT4.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: RTX 40608GB · +2.1 RTX 4060 Ti 8G8GB · +2.1 Radeon RX 9060 XT 8G8GB · +2.1 RTX 5060 Ti 8G8GB · +2.1 RTX 50608GB · +2.1
Tight but possible: Mac M1 8G8GB Mac M3 8G8GB
Ships in: GGUF AWQ
Compared here: GGUF vs AWQ
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-11-11 RSS → /feed.xml