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

Libraries

How to use AxionML/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="AxionML/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("AxionML/GLM-5.3-Flash-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("AxionML/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 AxionML/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 "AxionML/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": "AxionML/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/AxionML/GLM-5.3-Flash-NVFP4

SGLang

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

AxionML GLM-5.3-Flash-NVFP4

Mirrored by AxionML for open-source serving and deployment use cases. Part of AxionML's effort to provide ready-to-serve quantized models for the community.

Quantized by RadixArk. The weights in this repository are an unmodified copy of RadixArk/GLM-5.3-Flash-NVFP4 (revision f46cf340d35a22d0d83d0c1dac8957cf2b1bcd35). All credit for the quantization belongs to RadixArk.

This is an NVFP4-quantized version of zai-org/GLM-5.3-Flash (320B total parameters, 18B activated), quantized with NVIDIA Model Optimizer.

About NVFP4 quantization: NVFP4 on Blackwell couples a compact E2M1 FP4 codebook with blockwise FP8 (E4M3) scaling over 16-element micro-blocks, so that 4-bit stored values remain numerically useful for neural-network computation. The E2M1 codebook provides a small, nonuniform set of representable magnitudes up to ±6 and relies on saturating behavior rather than IEEE NaN/Inf encodings to maximize usable range per bit. Using an FP8 block scale (rather than power-of-two-only E8M0) enables fractional scales and error-minimizing scale selection. On Blackwell Tensor Cores, native FP4 multipliers exploit E2M1 simplicity while higher-precision FP32 accumulation protects dot-product accuracy.

Ready for commercial and non-commercial use under MIT.

Model Summary


Architecture	Natively multimodal hybrid-attention MoE: KDA linear attention, DSA sparse attention with indexer, MLA, manifold-constrained hyper-connections
Total Parameters	320B
Activated Parameters	18B
Layers / Experts	45 layers (3 dense + 42 MoE), 288 routed experts + shared expert, native MTP/NextN layer
Input	Text, image, video
Context Length	1,048,576 tokens
Checkpoint Size	~203 GB

Evaluation Results

Benchmark	Protocol	NVFP4
GSM8K	Full 1,319 × 4 seeds	97.14
AIME 2026	30 × 16 × 4 seeds	92.45
Terminal-Bench 2.1	89 tasks, terminus-2, pass@1	83.1

Scores reported by RadixArk for this checkpoint (SGLang, 4x GB300, FP8 KV cache, NEXTN speculative decoding). Text-only evaluations.

Quantization Details

Quantization format: NVFP4 W4A4 (group size 16, abs-max scaling) on gate_proj / up_proj / down_proj of all routed experts, the shared expert and the dense MLPs in layers 0–2
Unchanged: all attention (KDA, DSA indexer, MLA), hyper-connections, norms, routers, vision tower, MTP layer, embeddings and lm_head; KV cache not quantized in the checkpoint (FP8 KV validated at serving time)
Calibration dataset: 1,024 cnn_dailymail samples, length 512
Tool: NVIDIA Model Optimizer 0.46.0

Usage

Deploy with SGLang

python3 -m sglang.launch_server \
    --model-path AxionML/GLM-5.3-Flash-NVFP4 \
    --quantization modelopt_fp4 \
    --tp-size 4 \
    --dsa-prefill-backend trtllm \
    --dsa-decode-backend trtllm \
    --kv-cache-dtype fp8_e4m3 \
    --moe-runner-backend flashinfer_cutlass \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 5 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 6 \
    --speculative-adaptive \
    --reasoning-parser glm45 \
    --tool-call-parser glm47

Use the lmsysorg/sglang:glm-5.3-flash image. Audit evidence (tensor-audit-b.json, precision-contract-b.json) is included in this repository.

Limitations

The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the original model card and the upstream quantized model card for full details.

Credits

Base model: zai-org/GLM-5.3-Flash
Quantization: RadixArk/GLM-5.3-Flash-NVFP4 by RadixArk
Mirror: AxionML

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Safetensors

Model size

168B params

Tensor type

F32

BF16

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

Base model

zai-org/GLM-5.3-Flash

Quantized

(127)

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