How much memory do I need to run GLM-5.2 (UD-Q4_K_M)?

The file size is 466 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 467 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.

Does GLM-5.2 run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?

8 GB: no quantization fits; 12 GB: no quantization fits; 16 GB: no quantization fits; 24 GB: no quantization fits (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).

What is the command to run GLM-5.2 with Ollama?

Run "ollama run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.

zai-org

GLM-5.2

Parameters: 753.9B
Max context length: 1,048,576
Architecture: glm-dsa
License: Commercial OK

Base model: zai-org/GLM-5.2
License: mit
llama.cpp: b8036+

Memory needed to run UD-Q4_K_M at 8K tokens

467 GB Estimated

File size 466 GB Measured

VRAM it fits in (rough guide)

8 GB does not fit
12 GB does not fit
16 GB does not fit
24 GB does not fit
32 GB does not fit
48 GB does not fit

The smallest, UD-IQ1_S, needs an estimated 218 GB at 8K.

Macs cap how much memory the GPU can use, so leave headroom in unified memory.

Data as of Nov 9, 2026

About GLM-5.2

GLM-5.2 is a GGUF model based on zai-org/GLM-5.2 on Hugging Face. GGUF files are published by unsloth/GLM-5.2-GGUF. It has about 753.9B parameters, the glm-dsa architecture, a maximum context length of 1,048,576 tokens (values written in the GGUF).

There are 22 quantizations, with file sizes from 217 GB (UD-IQ1_S) to 1,508 GB (BF16) (actual sizes on Hugging Face).

Running UD-Q4_K_M (466 GB) with an 8,192-token context needs an estimated 467 GB of memory, and an estimated 470 GB at 32,768 tokens. The smallest, UD-IQ1_S (217 GB), needs an estimated 218 GB at 8,192 tokens.

The original model is licensed under MIT License, which allows commercial use (keep the license text and copyright notice when redistributing). Check the original license for the terms of use.

File size and estimated memory per quantization

Up to 8 GB
Up to 16 GB
Up to 24 GB
Up to 48 GB
Up to 128 GB
More

File sizes are the actual values on Hugging Face. Memory is estimated for each context length (KV cache f16). The formula is in How memory is calculated.

All memory figures are Estimated (KV cache f16). It should run on a setup with at least this much VRAM (or Mac unified memory). Click a value to see the devices and models that fit that amount (Find by GPU / Mac).

About the "quality guide" (how far a quantization drifts from the original)

For each quantization type, how far the output drifts from the original model, grouped into six levels from values llama.cpp measured on Llama-3-8B.

The value is mean KLD (the difference between the original and quantized model in next-token probabilities); smaller means closer to the original. Quantizations made with an imatrix (importance data) come out slightly smaller. Levels use the value without an imatrix (or with one, for types that only have that).

These values were not measured on this model. Larger models and MoE models tend to drift less with the same type. Distributor-specific mixed types (such as UD-Q4_K_XL) cannot be matched by type name alone, so they show "-".

Quantization	Quality guide	KLD (no imatrix)	KLD (imatrix)	Size (Llama-3-8B)
Q8_0	Nearly identical	0.0014	-	7.96 GiB
Q6_K	Very small	0.0055	-	6.14 GiB
Q5_K_M	Very small	0.0108	-	5.33 GiB
Q5_K_S	Small	0.0166	-	5.21 GiB
Q5_1	Small	0.0180	-	5.65 GiB
Q5_0	Small	0.0222	-	5.21 GiB
Q4_K_M	Small	0.0313	0.0282	4.58 GiB
Q4_K_S	Medium	0.0431	0.0320	4.37 GiB
IQ4_NL	Small	-	0.0357	4.35 GiB
IQ4_XS	Small	-	0.0363	4.14 GiB
Q4_1	Medium	0.0717	-	4.78 GiB
Q4_0	Medium	0.0719	-	4.34 GiB
Q3_K_L	Medium	0.0872	0.0731	4.03 GiB
Q3_K_M	Medium	0.1019	0.0844	3.74 GiB
IQ3_M	Medium	-	0.1025	3.53 GiB
IQ3_S	Medium	-	0.1113	3.42 GiB
IQ3_XS	Large	-	0.1387	3.28 GiB
IQ3_XXS	Large	-	0.1836	3.05 GiB
Q3_K_S	Large	0.2282	0.1998	3.41 GiB
IQ2_M	Large	-	0.3260	2.74 GiB
Q2_K	Very large	0.4451	0.3322	2.96 GiB
Q2_K_S	Very large	-	0.4034	2.96 GiB
IQ2_S	Very large	-	0.4392	2.56 GiB
IQ2_XS	Very large	-	0.5463	2.43 GiB
IQ2_XXS	Very large	-	0.8120	2.24 GiB
IQ1_M	Very large	-	1.3931	2.01 GiB
IQ1_S	Very large	-	2.2546	1.88 GiB

Source: llama.cpp (MIT), measured on Llama-3-8B against FP16, Wikitext-2, revision f364eb6f. Original table · Reading and choosing quantizations

GGUF source

unsloth/GLM-5.2-GGUF

Converted and published by a third party About this label

Last checked: Nov 9, 2026

Architecture: glm-dsa; llama.cpp b8036 and later
Parameters: 753.9B
Max context length: 1,048,576
License: mit Commercial OK

File size and memory per quantization for GLM-5.2 (unsloth/GLM-5.2-GGUF)
Quantization	File size Measured	Quality guide	4,096	8,192	16,384	32,768	65,536	131,072	262,144	1,048,576 max
UD-IQ1_S (6 parts)	217 GB	-	218 GB	218 GB	219 GB	221 GB	223 GB	229 GB	241 GB	312 GB
UD-IQ1_M (6 parts)	228 GB	-	230 GB	230 GB	231 GB	232 GB	235 GB	241 GB	253 GB	324 GB
UD-IQ2_XXS (6 parts)	238 GB	-	240 GB	240 GB	241 GB	242 GB	245 GB	251 GB	263 GB	334 GB
UD-IQ2_M (6 parts)	239 GB	-	240 GB	240 GB	241 GB	242 GB	245 GB	251 GB	263 GB	334 GB
UD-Q2_K_XL (7 parts)	254 GB	-	255 GB	255 GB	256 GB	258 GB	261 GB	267 GB	278 GB	349 GB
UD-IQ3_XXS (7 parts)	282 GB	-	283 GB	283 GB	284 GB	285 GB	288 GB	294 GB	306 GB	377 GB
UD-IQ3_S (8 parts)	309 GB	-	310 GB	310 GB	311 GB	312 GB	315 GB	321 GB	333 GB	404 GB
UD-Q3_K_M (9 parts)	343 GB	-	344 GB	344 GB	345 GB	347 GB	349 GB	355 GB	367 GB	438 GB
UD-Q3_K_XL (9 parts)	343 GB	-	344 GB	345 GB	345 GB	347 GB	350 GB	356 GB	367 GB	438 GB
UD-IQ4_XS (9 parts)	365 GB	-	367 GB	367 GB	368 GB	369 GB	372 GB	378 GB	390 GB	460 GB
UD-IQ4_NL (9 parts)	373 GB	-	374 GB	374 GB	375 GB	376 GB	379 GB	385 GB	397 GB	468 GB
UD-Q4_K_S (10 parts)	436 GB	-	438 GB	438 GB	439 GB	440 GB	443 GB	449 GB	461 GB	532 GB
UD-Q4_K_M (11 parts)	466 GB	-	467 GB	467 GB	468 GB	470 GB	473 GB	478 GB	490 GB	561 GB
UD-Q4_K_XL (11 parts)	467 GB	-	469 GB	469 GB	470 GB	471 GB	474 GB	480 GB	492 GB	562 GB
UD-Q5_K_S (12 parts)	527 GB	-	528 GB	529 GB	530 GB	531 GB	534 GB	540 GB	552 GB	622 GB
UD-Q5_K_M (13 parts)	561 GB	-	562 GB	562 GB	563 GB	565 GB	568 GB	573 GB	585 GB	656 GB
UD-Q5_K_XL (13 parts)	562 GB	-	564 GB	564 GB	565 GB	566 GB	569 GB	575 GB	587 GB	658 GB
UD-Q6_K (14 parts)	626 GB	-	627 GB	627 GB	628 GB	630 GB	633 GB	638 GB	650 GB	721 GB
UD-Q6_K_XL (16 parts)	684 GB	-	686 GB	686 GB	687 GB	688 GB	691 GB	697 GB	709 GB	779 GB
Q8_0 (17 parts)	801 GB	Nearly identical	803 GB	803 GB	804 GB	805 GB	808 GB	814 GB	826 GB	896 GB
UD-Q8_K_XL (18 parts)	820 GB	-	821 GB	821 GB	822 GB	823 GB	826 GB	832 GB	844 GB	915 GB
BF16 (33 parts)	1,508 GB	Original precision	1,509 GB	1,510 GB	1,510 GB	1,512 GB	1,515 GB	1,521 GB	1,532 GB	1,603 GB

Ollama  ollama run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M

llama.cpp  llama-server -hf unsloth/GLM-5.2-GGUF:UD-Q4_K_M

Third-party evaluations

Values from other organizations for the original model before quantization. Not this site's own rating. Values are for the original model before quantization. GGUF quantization (especially small quantizations) can lower performance.

Capabilities (Epoch AI)

153.0 151.3–154.9

GLM-5.2 · Values as of Jun 16, 2026

Source: Epoch Capabilities Index (Epoch AI) (CC BY 4.0). Original leaderboard · See in ranking

License and commercial use

MIT License Commercial OK

Allows commercial use, modification and redistribution. When redistributing, keep the copyright notice and license text.

Based on the license on the original model's Hugging Face page. Original license text (checked Oct 18, 2026)

The summary states facts this site read from the original text and is not legal advice. Always check the original license and its latest terms before use. All licenses

How to run

Mainline llama.cpp can load this model's format (glm-dsa) from b8036 (Feb 13, 2026) onward. Ollama and LM Studio bundle llama.cpp internally, so an older app fails to load it with errors such as "unknown model architecture". If that happens, update the app.

Example commands using UD-Q4_K_M. To use another quantization, replace the name with one from the table above. How to run local LLMs

Ollama

Run it by pointing directly at the GGUF on Hugging Face.

ollama run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M

llama.cpp

Pass the repository and quantization name to llama-server (or llama-cli) with -hf.

llama-server -hf unsloth/GLM-5.2-GGUF:UD-Q4_K_M

LM Studio

Paste unsloth/GLM-5.2-GGUF into the search box, download it, and choose the quantization from the file list.

Frequently asked questions

How much memory do I need to run GLM-5.2 (UD-Q4_K_M)?: The file size is 466 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 467 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
Can GLM-5.2 be used commercially?: The original model's license is MIT License (Commercial use allowed).Allows commercial use, modification and redistribution. When redistributing, keep the copyright notice and license text. Check the original license text before deciding.
Does GLM-5.2 run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?: 8 GB: no quantization fits; 12 GB: no quantization fits; 16 GB: no quantization fits; 24 GB: no quantization fits (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).
What is the command to run GLM-5.2 with Ollama?: Run "ollama run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.