---

license: other

license_name: glm-5.3

license_link: https://huggingface.co/zai-org/GLM-5.3/blob/main/LICENSE

base_model:

• zai-org/GLM-5.3

base_model_relation: quantized

quantized_by: AtomicChat

language:

• en
• zh

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• glm
• glm-5
• zai-org
• moe
• gguf
• imatrix
• quantized
• llama.cpp

---

How to Run GLM-5.3 Locally

<p style="margin-top: 0; margin-bottom: 0;">

<em>Built from Z.ai's original weights with our own importance matrix. The <a href="https://huggingface.co/datasets/AtomicChat/calib-corpora">calibration corpora</a> behind our builds are public.</em>

</p>

<div style="display: flex; gap: 8px; align-items: center; margin-top: 10px; margin-bottom: 10px;">

<a href="https://atomic.chat/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_glm_5_3&utm_content=btn_atomic"><img src="https://huggingface.co/AtomicChat/GLM-5.3-GGUF/resolve/main/btn_atomic.png" width="162" alt="Atomic Chat"></a>

<a href="https://discord.gg/8wGSsvmg4V"><img src="https://huggingface.co/AtomicChat/GLM-5.3-GGUF/resolve/main/btn_discord.png" width="119" alt="Discord"></a>

<a href="https://github.com/AtomicBot-ai/Atomic-Chat"><img src="https://huggingface.co/AtomicChat/GLM-5.3-GGUF/resolve/main/btn_github.png" width="115" alt="GitHub"></a>

</div>

<ul style="margin: 0 0 12px 0;">

<li>GLM-5.3 keeps the GLM-5.2 base and takes every gain from post-training. Per Z.ai it is the most capable open-weights model for coding, with open-source SOTA on Terminal Bench 3.0 and Agents' Last Exam.</li>

<li>You can now run GLM-5.3 in <a href="https://atomic.chat/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_glm_5_3&utm_content=bullet_app">Atomic Chat</a> with toggles for Low, High and Max thinking.</li>

<li>The quants are still uploading and need a llama.cpp build with GLM-5.3 support; Atomic Chat runs it as support ships.</li>

</ul>

<hr style="margin: 0 0 16px 0;">

<img src="https://huggingface.co/AtomicChat/GLM-5.3-GGUF/resolve/main/hero.png" alt="Z.ai" style="width:150px; max-width:100%; height:auto;"/>

Highlights

• 753B parameters, read from the published weight index. Mixture-of-Experts: 256 routed experts with 8 active per token plus 1 shared, 78 layers, the first 3 dense.
• Same base as GLM-5.2. Z.ai state every gain comes from post-training, and the two checkpoints carry an identical parameter count.
• Coding: per Z.ai a 50% improvement over GLM-5.2 on their in-house Z.ai Code Bench, and open-source SOTA on Terminal Bench 3.0 and Agents' Last Exam.
• Emergent cyber capability: Z.ai report state of the art on CyberGym for vulnerability discovery, with gains largest further up the exploitation chain, more than doubling GLM-5.2 on exploitation benchmarks.
• Thinking budget controlled by reasoning_effort with three levels, low, high and max. It defaults to max.
• Sparse attention with a learned indexer (glm_moe_dsa, top-2048 index), and one multi-token-prediction block in the checkpoint.
• Bilingual, English and Chinese.
• Ships in FP8 upstream, so our GGUF base is converted from the FP8 checkpoint rather than from BF16.
• Full imatrix quantization with our public calibration corpora.

> [!NOTE]

> These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

> [!IMPORTANT]

> This is a 753B model. Even at four bits a full set of weights runs to several

> hundred gigabytes, and all of it has to fit in fast memory. In practice that

> means a large-RAM server or a serious multi-GPU rig, not a laptop and not most

> workstations.

> [!IMPORTANT]

> Always pass --jinja so the GLM-5.3 chat template is applied. Z.ai note that

> clear_thinking defaults to false in that template, so pass clear_thinking=true

> for chat scenarios.

Model Overview

| Property | Value |

|---|---|

| Base model | zai-org/GLM-5.3 |

| Parameters | 753B total, MoE with 8 routed experts plus 1 shared active per token |

| Layers | 78, the first 3 dense, 256 routed experts in the rest |

| Architecture | GlmMoeDsaForCausalLM, sparse attention with a learned indexer |

| Upstream precision | FP8 (e4m3, 128x128 block scales) |

| Vocabulary | 154,880 |

| Context length | 1,048,576 positions in the config; Z.ai evaluate up to 1M with context management |

| Languages | English, Chinese |

| Thinking | reasoning_effort: low, high, max. Defaults to max |

| This repo | GGUF quants (imatrix). The importance matrix we built is published here too |

<img src="https://huggingface.co/AtomicChat/GLM-5.3-GGUF/resolve/main/benchmark.png" alt="GLM-5.3 benchmark scores" style="width:100%; max-width:900px;"/>

Scores are Z.ai's published results for the base zai-org/GLM-5.3. Selected numbers from

their table: Terminal Bench 2.1 88.2, Terminal Bench 3.0 28.3, DeepSWE 1.1 66.9,

CyberGym 84.5, AutomationBench 48.2, HLE w/ tools 62.5, GDPval-AA v2 1769.

Choosing a quant

| Quant | Size | Notes |

|---|---|---|

| AD-IQ2_M | — | Smallest usable. Aggressive low-bit for the tightest boxes. |

| AD-IQ3_M | — | Beats Q3 at similar size thanks to imatrix. Best low-memory pick. |

| AD-Q4_K_M | — | Recommended default. Best balance of size, speed and quality. |

| AD-Q5_K_M | — | A step up when the memory is there. |

| AD-Q6_K | — | Near lossless. |

| AD-Q8_0 | — | Effectively lossless, reference quality. |

> [!TIP]

> Sizes fill in once the quants finish uploading. Pick the largest file that fits your

> memory with room for context.

AD- marks an Atomic Dynamic layout: bits are assigned per tensor role rather than left

to a preset, with the router and the shared expert held high and the routed experts

carrying the compression.

Get started

> [!NOTE]

> GLM-5.3 uses the glm_moe_dsa architecture. The quants in this repo are still

> uploading, and running them needs a llama.cpp build that has landed GLM-5.3 support.

> Until then, Atomic Chat is the easiest way to run it as support ships.

Run GLM-5.3 locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/GLM-5.3-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/GLM-5.3-GGUF:AD-Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/GLM-5.3-GGUF:AD-Q4_K_M
• LM Studio / Jan: search the repo id, download any quant.

For the original FP8 weights rather than GGUF, Z.ai list SGLang, vLLM, TokenSpeed,

Transformers and KTransformers on the base model card.

Best practices

| Parameter | Value |

|---|---|

| temperature | 1.0 |

| top_p | 0.95 |

| reasoning_effort | max for benchmark reproduction, low or high to spend fewer tokens |

| clear_thinking | true for chat |

From Z.ai's evaluation settings (HLE w/ tools). Per-benchmark settings vary; see the base

model card for details.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/GLM-5.3-GGUF:AD-Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download zai-org/GLM-5.3 (original FP8 weights).
2. Convert to GGUF with a llama.cpp build that supports the GLM-5.3 architecture (glm_moe_dsa, sparse attention with a learned indexer).
3. Build an importance matrix over our public calibration corpora.
4. Quantize the ladder with --imatrix, assigning bits per tensor role: router and shared expert high, routed experts carrying the compression.

License

Released by Z.ai (zai-org) under the GLM-5.3 license. Quantized by Atomic Chat.