Text model · GLM

GL GLM-4-32B-0414: RAM and VRAM requirements

GLM-4-32B-0414 needs about 20.5 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~18.33 GB to download; KV cache and overhead add the rest), or about 34.4 GB at Q8_0. The lightest hardware that runs it is Nvidia GeForce RTX 4090 (24GB).

GLM family · 32B params · released Apr 2025.

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License MIT · Commercial OK

Q4_K_M GGUF

18.33 GB

Q8_0 GGUF

32.24 GB

Memory @ Q4 (4k)

~20.5 GB

Context

32 k

Q4_K_M and Q8_0 are GGUF download (file) sizes. Memory @ Q4 includes the KV cache and overhead, so it runs larger than the file on disk.

Will it run on your device?

GLM-4-32B-0414 runs on 13 of 40 tracked devices at Q4_K_M.

12 run well 1 tight fit 27 too small

Fit check Q4_K_M

Your device

Apple M4 (16GB)

No, not enough memory

needs 20.5 GB usable 10.5 GB

See the full breakdown

Memory breakdown

Weights (Q4_K_M)18.33 GB

KV cache (4k)1.4 GB

Overhead0.8 GB

Total20.5 GB

How context length changes it

4k context ~20.5 GB 32k context ~30 GB 128k context ~62.5 GB

Longer context grows the KV cache quickly: GLM-4-32B-0414 needs ~20.5 GB at 4k but ~62.5 GB at 128k, which can push it past a device that fits it at a short context.

Speed drops too: every token re-reads the KV cache, so at 128k context GLM-4-32B-0414 generates at roughly ~30% of its short-context speed. How this is estimated.

Quantization sizes

GGUF quantson disk

Quantization	Size on disk
Q2_K	13.4 GB est
Q3_K_M	15.6 GB est
Q4_K_M (default)	18.33 GB
Q5_K_M	22.8 GB est
Q6_K	26.2 GB est
Q8_0	32.24 GB
FP16	65.1 GB

Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.

Run it

llama.cpp

$ llama-cli -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M

LM Studio

$ lms get bartowski/THUDM_GLM-4-32B-0414-GGUF

Which devices can run GLM-4-32B-0414?

Apple Silicon Macs

RAM-only laptops

iPhone & iPad

Android

NVIDIA GPUs

AMD GPUs

Same job, different size

Pick by what fits your memory: step down to free up VRAM, or step up if you have headroom.

Next size down North Mini Code 1.0 30.5B Next size up Gemma 4 31B 32.7B

Similar models

Head-to-head

FAQ

How much VRAM or RAM does GLM-4-32B-0414 need?

At Q4_K_M, GLM-4-32B-0414 needs about 20.5 GB (weights ~18.33 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~34.4 GB.

What is the Q4_K_M GGUF file size of GLM-4-32B-0414?

The Q4_K_M GGUF file is about 18.33 GB to download, and the Q8_0 GGUF is about 32.24 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~20.5 GB of memory at Q4_K_M.

Can GLM-4-32B-0414 run on a laptop?

GLM-4-32B-0414 is large; you need a 24 GB+ GPU or a 32-48 GB Mac at Q4_K_M.

Can I use GLM-4-32B-0414 commercially?

Yes. GLM-4-32B-0414 is licensed MIT, which permits commercial use.

Understand the numbers

Short guides to the ideas behind GLM-4-32B-0414's memory and quant figures.

Dense 32B transformer. Native context 32K (max_position_embeddings=32768 in config.json); a separate GLM-4-32B-0414-128K variant extends to 128K via YaRN rope scaling. Default_context_k set to 32 for the base model. MIT license per both THUDM and zai-org HF repos. Released April 14 2025 (confirmed via zai-org/GLM-4 GitHub). Q4_K_M 19.7 GB, Q8_0 34.6 GB, FP16 65.1 GB all measured from bartowski GGUF file listing. No official ollama.com/library tag; community tags exist (JollyLlama/GLM-4-32B-0414-Q4_K_M, sammcj/glm-4-32b-0414).

Sources

Last validated 2026-08-03. Memory figures are estimates. See methodology.