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GLM-5.3-GGUF
by unsloth · source Hugging Face · updated 2026-08-29
other45 GB~53 GB RAMsource aliveunlabeled
See Unsloth Dynamic 3.0 GGUFs for our quantization benchmarks. You can now run GLM-5.3 in Unsloth Desktop with toggles for Low, High and Max thinking. Read our GLM-5.3 guide for analysis and instruct…
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Source and license
- Source: https://huggingface.co/unsloth/GLM-5.3-GGUF
- License: other (custom license: read it at the source before installing)
- Requirements: about 53 GB of RAM, 45 GB on disk (estimate: suggested file size × 1.15 + 0.5 GB; a real measurement comes with lsh models). Runs with llama.cpp, ollama.
- Tags:
transformersggufunslothglm_moe_dsatext-generationenzhendpoints_compatibleconversational
Numbers
- 577,370 downloads on Hugging Face
- 97 likes
- license other
- 45 GB for Q8_0/GLM-5.3-Q8_0-00001-of-00017.gguf
- 130,156 stars on ggml-org/llama.cpp
- 2,528 open issues and PRs
- last release v0.5.0 on 2026-09-23
- 327,838 npm downloads a week for node-llama-cpp
- latest node-llama-cpp@3.22.1
Numbers as of 2026-10-02 20:52 UTC, from the source API.
Summary
Summary not ready yet: the numbers are here, the text is not. It is written by the collector through the LogiShell model facade when a provider key is present.
Reviews
No reviews yet. Reviews are written inside LogiShell: open this card in the app.
Files
| file | quant | size |
|---|---|---|
BF16/GLM-5.3-BF16-00001-of-00033.gguf | BF16 | 42 GB |
BF16/GLM-5.3-BF16-00002-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00003-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00004-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00005-of-00033.gguf | BF16 | 44 GB |
BF16/GLM-5.3-BF16-00006-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00007-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00008-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00009-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00010-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00011-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00012-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00013-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00014-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00015-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00016-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00017-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00018-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00019-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00020-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00021-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00022-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00023-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00024-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00025-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00026-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00027-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00028-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00029-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00030-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00031-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00032-of-00033.gguf | BF16 | 43 GB |
BF16/GLM-5.3-BF16-00033-of-00033.gguf | BF16 | 31 GB |
Q8_0/GLM-5.3-Q8_0-00001-of-00017.gguf | Q8_0 | 45 GB |
Q8_0/GLM-5.3-Q8_0-00002-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00003-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00004-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00005-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00006-of-00017.gguf | Q8_0 | 45 GB |
Q8_0/GLM-5.3-Q8_0-00007-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00008-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00009-of-00017.gguf | Q8_0 | 45 GB |
Q8_0/GLM-5.3-Q8_0-00010-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00011-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00012-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00013-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00014-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00015-of-00017.gguf | Q8_0 | 45 GB |
Q8_0/GLM-5.3-Q8_0-00016-of-00017.gguf | Q8_0 | 46 GB |
Q8_0/GLM-5.3-Q8_0-00017-of-00017.gguf | Q8_0 | 16 GB |
UD-IQ1_M/GLM-5.3-UD-IQ1_M-00001-of-00006.gguf | IQ1_M | 9 MB |
UD-IQ1_M/GLM-5.3-UD-IQ1_M-00002-of-00006.gguf | IQ1_M | 46 GB |
UD-IQ1_M/GLM-5.3-UD-IQ1_M-00003-of-00006.gguf | IQ1_M | 46 GB |
UD-IQ1_M/GLM-5.3-UD-IQ1_M-00004-of-00006.gguf | IQ1_M | 46 GB |
UD-IQ1_M/GLM-5.3-UD-IQ1_M-00005-of-00006.gguf | IQ1_M | 46 GB |
UD-IQ1_M/GLM-5.3-UD-IQ1_M-00006-of-00006.gguf | IQ1_M | 29 GB |
UD-IQ1_S/GLM-5.3-UD-IQ1_S-00001-of-00006.gguf | IQ1_S | 9 MB |
UD-IQ1_S/GLM-5.3-UD-IQ1_S-00002-of-00006.gguf | IQ1_S | 47 GB |
UD-IQ1_S/GLM-5.3-UD-IQ1_S-00003-of-00006.gguf | IQ1_S | 46 GB |
UD-IQ1_S/GLM-5.3-UD-IQ1_S-00004-of-00006.gguf | IQ1_S | 46 GB |
From the source README
Read our How to Run GLM-5.3 Guide!
See Unsloth Dynamic 3.0 GGUFs for our quantization benchmarks.
You can now run GLM-5.3 in Unsloth Desktop with toggles for Low, High and Max thinking.
Read our GLM-5.3 guide for analysis and instructions.
GLM-5.3
GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks:
+ Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam.
+ Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.
Benchmark
| Benchmark | GLM-5.3 | GLM-5.2 | Kimi K3 | DeepSeek-V4 Pro-0813 | Qwen3.8-Max | Opus 4.8 | Fable 5 (w/ fallback) | GPT-5.6 Sol |
|------------------------------|-----------|---------|----------|----------------------|-------------|----------|-----------------------|---------------|
| Terminal Bench 2.1 | 88.2 | 81.0 | 88.3 | 87.9 | 86.6 | 85.0 | 88.0 | 88.8 |
| Terminal Bench 3.0 | 28.3 | 4.6 | 17.4 | – | – | 21.1 | 33.7 | 34.6 |
| DeepSWE (v1.1) | 66.9 | 46.2 | 67.5 | 62.7 | 56.6 | 58.0 | 69.7 | 72.7 |
| NL2Repo | 58.0 | 48.9…
Source: https://huggingface.co/unsloth/GLM-5.3-GGUF
Card id model:hf:unsloth/GLM-5.3-GGUF · collected 2026-10-02 20:52 UTC · JSON