GLM-5.3-Flash-GGUF

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

<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

[!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

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.