Run unsloth/GLM-5.2-GGUF locally

License: mit ⬇ 392,857 ❤ 512
Parameters753.86B
Context1,048,576

unsloth/GLM-5.2-GGUF is a very large language model with 753.86 billion parameters, built on the glm-dsa architecture. It is released under the mit license and has been downloaded 392,857 times.

To run unsloth/GLM-5.2-GGUF locally at a 4,096-token context, its quantized versions need between 202.75 GB (IQ1_S, lowest quality) and 1405.34 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.

→ Guide: How much VRAM do you need?

All quantizations

Quant.Bits QualityWeights KVTotal Speed~Verdict
IQ1_S 2.3 Very low 201.83 GB 0.12 GB 202.75 GB Insufficient
IQ1_M 2.42 Very low 212.8 GB 0.12 GB 213.72 GB Insufficient
IQ2_XXS 2.53 Very low 222.08 GB 0.12 GB 223.0 GB Insufficient
IQ2_M 2.53 Very low 222.19 GB 0.12 GB 223.11 GB Insufficient
Q2_K_XL 2.69 Low 236.44 GB 0.12 GB 237.36 GB Insufficient
IQ3_XXS 2.99 Low 262.34 GB 0.12 GB 263.26 GB Insufficient
IQ3_S 3.28 Low 287.44 GB 0.12 GB 288.36 GB Insufficient
Q3_K_M 3.64 Fair 319.2 GB 0.12 GB 320.11 GB Insufficient
Q3_K_XL 3.64 Fair 319.41 GB 0.12 GB 320.33 GB Insufficient
IQ4_XS 3.88 Fair 340.22 GB 0.12 GB 341.14 GB Insufficient
IQ4_NL 3.95 Fair 347.07 GB 0.12 GB 347.98 GB Insufficient
Q4_K_S 4.63 Good 406.46 GB 0.12 GB 407.37 GB Insufficient
Q4_K_M 4.94 Good 433.83 GB 0.12 GB 434.75 GB Insufficient
Q4_K_XL 4.96 Good 435.2 GB 0.12 GB 436.11 GB Insufficient
Q5_K_S 5.6 Very good 491.05 GB 0.12 GB 491.96 GB Insufficient
Q5_K_M 5.95 Very good 522.31 GB 0.12 GB 523.23 GB Insufficient
Q5_K_XL 5.97 Very good 523.84 GB 0.12 GB 524.75 GB Insufficient
Q6_K 6.64 Excellent 582.88 GB 0.12 GB 583.79 GB Insufficient
Q6_K_XL 7.26 Excellent 637.37 GB 0.12 GB 638.28 GB Insufficient
Q8_0 8.5 Excellent 746.32 GB 0.12 GB 747.24 GB Insufficient
Q8_K_XL 8.7 Excellent 763.41 GB 0.12 GB 764.32 GB Insufficient
BF16 16.0 Excellent 1404.42 GB 0.12 GB 1405.34 GB Insufficient

KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.

Frequently asked questions

Can I run unsloth/GLM-5.2-GGUF on an 8 GB GPU?

No. unsloth/GLM-5.2-GGUF does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.

Can I run unsloth/GLM-5.2-GGUF on a 16 GB GPU?

No. unsloth/GLM-5.2-GGUF does not fit on a 16 GB GPU, even with the smallest quantization and system RAM offloading.

Can I run unsloth/GLM-5.2-GGUF on a 24 GB GPU?

No. unsloth/GLM-5.2-GGUF does not fit on a 24 GB GPU, even with the smallest quantization and system RAM offloading.

What is the best quantization for unsloth/GLM-5.2-GGUF?

If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.