GLM 5.2 Vision NVFP4

GLM 5.2 Vision NVFP4 by baseten, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

Comparison

Feature	GLM 5.2 Vision NVFP4	Interfaze
Input Modalities	text, image	image, text, audio, video, document
Native OCR	No	Yes
Long Document Processing	Yes	Yes
Language Support	unknown	162+
Native Speech-to-Text	No	Yes
Native Object Detection	No	Yes
Guardrail Controls	No	Yes
Context Input Size	1M	1M
Tool Calling	Yes	Tool calling supported + built in browser, code execution and web search

Scaling

Feature	GLM 5.2 Vision NVFP4	Interfaze
Scaling	Self-hosted/Provider-hosted with quantization	Unlimited

View model card on Hugging Face

GLM-5.2 with sight. A vision-language model that bolts the MoonViT vision encoder from Kimi-K2.6 onto GLM-5.2 through a trained PatchMerger projector.

GLM-5.2 is a strong open reasoning model with no vision input. This checkpoint adds it, without touching a single GLM weight: the text backbone and the vision tower are both frozen and byte-identical to their upstream releases. The only newly-trained parameters are the 49.5M-parameter projector that maps MoonViT's 1152-dim patch embeddings into GLM's 6144-dim token space.

Component	Detail
Text backbone	GLM-5.2 (744B total / A40B active, MoE + MLA + DSA sparse attention) — frozen
Vision tower	MoonViT-3d from Kimi-K2.6, 27 layers, 1152-dim — frozen
Projector	PatchMerger MLP (`pre_norm → linear_1 → GELU → linear_2`), 1152→4608→6144 — trained
Text weights	NVFP4, from `nvidia/GLM-5.2-NVFP4`
Size	~466 GB
Hardware	8×B200, or 4×B200 at 256k context — Blackwell only
Image tokens	up to 4096 per image (16384 MoonViT patches, 2×2 merge)
Max context	1048576 (1M tokens)

Quickstart

SGLang needs a small out-of-tree plugin because Glm5vForConditionalGeneration is not yet an upstream architecture. It ships inside this repo, so there is nothing else to clone:

uvx --from huggingface-hub hf download baseten/GLM-5.2-Vision-NVFP4 \
  --include 'plugins/*' --local-dir ./glm5v
uv pip install ./glm5v/plugins

SGLang

export SGLANG_EXTERNAL_MODEL_PACKAGE=sglang_glm5v
export SGLANG_EXTERNAL_MM_PROCESSOR_PACKAGE=sglang_glm5v
export SGLANG_EXTERNAL_MM_MODEL_ARCH=Glm5vForConditionalGeneration
python -m sglang_glm5v.patch

8×B200 — full 1M context

python -m sglang.launch_server \
  --model-path baseten/GLM-5.2-Vision-NVFP4 --trust-remote-code \
  --tp-size 8 \
  --quantization modelopt_fp4 \
  --disable-shared-experts-fusion --disable-flashinfer-autotune \
  --attention-backend dsa --mm-attention-backend sdpa \
  --kv-cache-dtype fp8_e4m3 --page-size 64 \
  --mem-fraction-static 0.85 \
  --context-length 1048576 \
  --reasoning-parser glm45 --tool-call-parser glm47 \
  --served-model-name glm-5.2-vision \
  --port 30000

4×B200 — 256k context

Same command with --tp-size 4, a higher memory fraction, and a smaller context:

python -m sglang.launch_server \
  --model-path baseten/GLM-5.2-Vision-NVFP4 --trust-remote-code \
  --tp-size 4 \
  --quantization modelopt_fp4 \
  --disable-shared-experts-fusion --disable-flashinfer-autotune \
  --attention-backend dsa --mm-attention-backend sdpa \
  --kv-cache-dtype fp8_e4m3 --page-size 64 \
  --mem-fraction-static 0.90 \
  --context-length 262144 \
  --reasoning-parser glm45 --tool-call-parser glm47 \
  --served-model-name glm-5.2-vision \
  --port 30000

Query it

Standard OpenAI multimodal messages deliver the image as image_url:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="none")
r = client.chat.completions.create(
    model="glm-5.2-vision",
    messages=[{"role": "user", "content": [
        {"type": "image_url", "image_url": {"url": "https://ultralytics.com/images/bus.jpg"}},
        {"type": "text", "text": "Describe this image in detail."},
    ]}],
    temperature=1.0, top_p=0.95, max_tokens=512,
)
print(r.choices[0].message.content)

GLM-5.2 is a reasoning model: with --reasoning-parser glm45, the chain of thought arrives in message.reasoning_content and the answer in message.content.

Deploy on Baseten

The repository includes ready-to-push Truss configs. The only credential you need is an API key for your own Baseten account; no Hugging Face token or pre-created Baseten secret is required.

Install uv and create a Baseten API key.
Export the key, download the small Truss directory, and deploy one of the two configurations:

export BASETEN_API_KEY="your-baseten-api-key"
uvx truss login --api-key "$BASETEN_API_KEY" --remote baseten --non-interactive

uvx --from huggingface-hub hf download baseten/GLM-5.2-Vision-NVFP4 \
  --include 'truss/*' --local-dir ./glm5v
cd glm5v/truss


uvx truss push --remote baseten --config config_nvfp4_4gpu.yaml --wait --output json

The command creates a new model and published deployment in your Baseten account and prints JSON containing model_id, model_version_id, predict_url, and logs_url. It does not promote the deployment to production.

Set PREDICT_URL to the returned predict_url, then query the model:

export PREDICT_URL="https://model-...api.baseten.co/deployment/.../predict"

curl -fsS "$PREDICT_URL" \
  -H "Authorization: Api-Key $BASETEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.2-vision",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "https://ultralytics.com/images/bus.jpg"}},
        {"type": "text", "text": "Describe this image in detail."}
      ]
    }],
    "max_tokens": 512,
    "temperature": 1.0,
    "top_p": 0.95
  }'

The first deployment downloads about 466 GB of weights and initializes SGLang, so startup can take several minutes.

License

MIT, following both parents: GLM-5.2 (MIT) and Kimi-K2.6 (Modified MIT). The projector weights are released under MIT. Redistributed upstream weights remain under their original terms.

Acknowledgements

Built on Z.ai's GLM-5.2 and Moonshot AI's Kimi-K2.6. Neither team was involved in this work; please do not direct issues with this checkpoint to them.