Instructions to use ggml-org/GLM-5.3-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ggml-org/GLM-5.3-Flash-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama cli -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Use Docker

docker model run hf.co/ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Llama new
LM Studio
Jan

vLLM

How to use ggml-org/GLM-5.3-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ggml-org/GLM-5.3-Flash-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ggml-org/GLM-5.3-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Ollama
How to use ggml-org/GLM-5.3-Flash-GGUF with Ollama:
```
ollama run hf.co/ggml-org/GLM-5.3-Flash-GGUF:Q2_K
```
Unsloth Desktop

How to use ggml-org/GLM-5.3-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ggml-org/GLM-5.3-Flash-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ggml-org/GLM-5.3-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/ggml-org/GLM-5.3-Flash-GGUF:Q2_K
```

Lemonade

How to use ggml-org/GLM-5.3-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Run and chat with the model

lemonade run user.GLM-5.3-Flash-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use ggml-org/GLM-5.3-Flash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ggml-org/GLM-5.3-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ggml-org/GLM-5.3-Flash-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

GLM-5.3-Flash

Run it in the Llama app: https://llama.app

Or from the terminal:

llama serve -hf ggml-org/GLM-5.3-Flash-GGUF

Source models

https://huggingface.co/zai-org/GLM-5.3-Flash-BF16

Notes

320B MoE (18B active), 288 routed + 1 shared expert per layer, mHC + DSA, 1 MTP layer.
Q4_K: all routed experts at Q4_K.
Q2_K: routed down experts at Q4_K, gate/up experts at Q2_K.
Q2_K_S: all routed experts at Q2_K (smallest).
Small non-expert tensors (embeddings, attention, shared experts, dense FFN) are kept at Q8_0.
Includes a Q8_0 mmproj for the vision encoder and MTP sidecars (Q8_0 / Q4_0) for speculative decoding.
The Q2_K / Q2_K_S expert tensors are calibrated with the imatrix from https://huggingface.co/AesSedai/GLM-5.3-Flash-GGUF

TODOs

add info

This model is automatically converted using https://github.com/ggml-org/convert

Downloads last month: 8,109

GGUF

Model size

313B params

Architecture

glm5-next

Hardware compatibility

2-bit

4-bit

8-bit

Model tree for ggml-org/GLM-5.3-Flash-GGUF

Base model

zai-org/GLM-5.3-Flash-BF16

Quantized

(38)

this model