Instructions to use RedHatAI/GLM-5.3-Flash-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/GLM-5.3-Flash-NVFP4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="RedHatAI/GLM-5.3-Flash-NVFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("RedHatAI/GLM-5.3-Flash-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/GLM-5.3-Flash-NVFP4", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/GLM-5.3-Flash-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/GLM-5.3-Flash-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/GLM-5.3-Flash-NVFP4",
		"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/RedHatAI/GLM-5.3-Flash-NVFP4

SGLang

How to use RedHatAI/GLM-5.3-Flash-NVFP4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RedHatAI/GLM-5.3-Flash-NVFP4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/GLM-5.3-Flash-NVFP4",
		"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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RedHatAI/GLM-5.3-Flash-NVFP4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/GLM-5.3-Flash-NVFP4",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/GLM-5.3-Flash-NVFP4 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/GLM-5.3-Flash-NVFP4
```

GLM-5.3-Flash-NVFP4

Model Overview

Model Architecture: Glm5NextForConditionalGeneration
- Input: Text / Image
- Output: Text
Model Optimizations:
- Weight quantization: FP4
- Activation quantization: FP4
Release Date: 2026-08-27
Version: 1.0
Model Developers: RedHatAI

This model is a quantized version of zai-org/GLM-5.3-Flash. It was evaluated on several tasks to assess its quality.

Model Optimizations

This model was obtained by quantizing the MoE expert weights and activations of zai-org/GLM-5.3-Flash to FP4 (NVFP4) data type, ready for inference with vLLM. The MTP layers are kept in FP8, matching the source checkpoint.

This optimization reduces the number of bits per parameter in the quantized layers from 8 to 4, reducing the disk size and GPU memory requirements by approximately 50% of the quantized weights.

Only the weights and activations of the MoE expert linear operators are quantized using LLM Compressor.

Deployment

vLLM Serving

docker run --gpus all \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -e VLLM_ENGINE_READY_TIMEOUT_S=3600 \
  vllm/vllm-openai:glm53-flash RedHatAI/GLM-5.3-Flash-NVFP4 \
  --tensor-parallel-size 4 \
  --no-enable-flashinfer-autotune \
  --tool-call-parser glm47 \
  --enable-auto-tool-choice \
  --reasoning-parser glm45

Enable Speculative Decoding

docker run --gpus all \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -e VLLM_ENGINE_READY_TIMEOUT_S=3600 \
  -e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
  vllm/vllm-openai:glm53-flash RedHatAI/GLM-5.3-Flash-NVFP4 \
  --tensor-parallel-size 4 \
  --no-enable-flashinfer-autotune \
  --tool-call-parser glm47 \
  --enable-auto-tool-choice \
  --reasoning-parser glm45 \
  --gpu-memory-utilization 0.85 \
  --disable-custom-all-reduce \
  --speculative-config '{"method":"mtp","num_speculative_tokens":5}'

Creation

This model was created by applying LLM Compressor with the NVFP4 scheme, exported in compressed-tensors format.

Evaluation

This model was evaluated on GSM8K Platinum, MATH-500, AIME 2025, and GPQA Diamond using lm-evaluation-harness and lighteval, all served with vLLM (OpenAI-compatible API). Each benchmark was run with 3 seeds (8 seeds for AIME 2025) and the results averaged.

Accuracy

Category	Benchmark	RedHatAI/GLM-5.3-Flash-NVFP4
Reasoning	GSM8K Platinum (strict-match)	97.74%
	MATH-500 (pass@1)	94.87%
	AIME 2025 (pass@1)	86.67%
	GPQA Diamond (pass@1)	90.57%

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Safetensors

Model size

169B params

Tensor type

F32

BF16

F8_E4M3

Model tree for RedHatAI/GLM-5.3-Flash-NVFP4

Base model

zai-org/GLM-5.3-Flash

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(115)

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Finetunes

2 models

Quantizations

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