---

license: mit

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

base_model:

• zai-org/GLM-5.3-Flash

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
• multimodal
• gguf
• imatrix
• quantized
• llama.cpp

---

How to Run GLM-5.3-Flash 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_flash&utm_content=btn_atomic"><img src="https://huggingface.co/AtomicChat/GLM-5.3-Flash-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-Flash-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-Flash-GGUF/resolve/main/btn_github.png" width="115" alt="GitHub"></a>

</div>

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

<li>GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series (320B total, 18B active).</li>

<li>These GGUFs are self-quantized from Z.ai's original weights with our own importance matrix, published alongside the quants.</li>

<li>The quants are still uploading and need a llama.cpp build with GLM-5.3-Flash 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-Flash-GGUF/resolve/main/hero.png" alt="Z.ai" style="width:150px; max-width:100%; height:auto;"/>

Highlights

• 320B total / 18B active Mixture-of-Experts. The first natively multimodal model in the GLM-5 series; per Z.ai it outperforms GLM-5.2 across benchmarks at about one-tenth the price, and approaches Claude Opus 4.8 on coding and agentic tasks.
• Hybrid attention: combines sparse and linear attention to sharply cut long-context serving cost while preserving precise long-context capability. A first for the GLM series.
• Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency.
• Newly trained base on a 30T-token multimodal pre-training corpus.
• Bilingual, English and Chinese.
• Natively multimodal (text and vision). These GGUF quants cover the text path.
• Frontier coding and agentic scores (Z.ai-reported): Terminal-Bench 2.1 84.3, DeepSWE 1.1 63.4, HLE w/ tools 55.3, AutomationBench 48.8.
• 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]

> Always pass --jinja so the GLM-5.3-Flash chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

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

| Total / active parameters | 320B total / 18B active |

| Architecture | Hybrid sparse + linear attention MoE with Manifold-Constrained Hyper-Connections (mHC) |

| Modality | Natively multimodal (text and vision); this repo covers the text path |

| Languages | English, Chinese |

| Pre-training | 30T-token multimodal corpus |

| Context length | Not stated by Z.ai; evaluations run up to 1,000,000 tokens with context management |

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

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

Scores are Z.ai's published results for the base zai-org/GLM-5.3-Flash. Quantization preserves the large majority of this; Q4_K_M and up sit within a point or two of full precision.

Choosing a quant

| Quant | Size | Notes |

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

| IQ2_M | — | Smallest usable. Aggressive low-bit for memory-constrained boxes. |

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

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

| UD-Q4_K_XL | — | Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint. |

| Q6_K | — | Near lossless. |

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

> [!TIP]

> Sizes fill in once the quants finish uploading. Pick the largest file that fits your (V)RAM with room for context.

Get started

> [!NOTE]

> GLM-5.3-Flash uses a new hybrid sparse + linear attention architecture with Manifold-Constrained Hyper-Connections. The quants in this repo are still uploading, and running them needs a llama.cpp build that has landed GLM-5.3-Flash support. Until then, Atomic Chat is the easiest way to run it as support ships.

Run GLM-5.3-Flash locally with:

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

Best practices

| Parameter | Value |

|---|---|

| temperature | 1.0 |

| top_p | 0.95 |

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/ggerganov/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-Flash-GGUF:UD-Q4_K_XL \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download zai-org/GLM-5.3-Flash (original weights).
2. Convert to GGUF with a llama.cpp build that supports the GLM-5.3-Flash architecture (hybrid sparse + linear attention, mHC).
3. Build an importance matrix over our public calibration corpora.
4. Quantize the ladder with --imatrix; UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

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