AtomicChat/GLM-5.3-Flash-GGUF

How to Run GLM-5.3-Flash Locally

Built from Z.ai's original weights with our own importance matrix. The calibration corpora behind our builds are public.

• GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series (320B total, 18B active).
• These GGUFs are self-quantized from Z.ai's original weights with our own importance matrix, published alongside the quants.
• The quants are still uploading and need a llama.cpp build with GLM-5.3-Flash support; Atomic Chat runs it as support ships.

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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.

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.

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.

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.

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

Get started

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.