Base model: zai-org/GLM-4.6V-Flash — 9B parameter vision-language model by Z.ai with 40 transformer layers, 4096 hidden dim, 32 attention heads (8 KV heads), SwiGLU activation. Paper: 2507.01006.
Vision encoder: 24-layer ViT (1536 hidden dim, 1536/4096 attention dim, 13696 intermediate FFN)
Context length: 128K tokens
Quantization: NVFP4 — NVIDIA 4-bit FP4 format with Per-Group UE4M3 scales (4.64 BPW, 5.08 GB)
Thinking: Enabled by default (native <think>/</think> tokens, opt-out via enable_thinking=false)

Files

File	Size	Description
`glm-4.6v-flash-nvfp4.gguf`	5.08 GB	Quantized text model (523 tensors, 4.64 BPW)
`mmproj-glm-4.6v-flash-f16.gguf`	1.66 GB	Vision encoder projector (182 tensors, F16)

Usage

LM Studio

Load both files — the text GGUF as the main model and the mmproj as the vision encoder. Supports multimodal inputs (images + text).

llama.cpp

bash

./llama-llava-cli \
  -m glm-4.6v-flash-nvfp4.gguf \
  --mmproj mmproj-glm-4.6v-flash-f16.gguf \
  -p "Describe this image in detail." \
  --image path/to/image.jpg

Python (llama-cpp-python)

python

from llama_cpp import Llama

llm = Llama(
    model_path="glm-4.6v-flash-nvfp4.gguf",
    mmproj="mmproj-glm-4.6v-flash-f16.gguf",
    n_ctx=32768
)

output = llm.create_chat_completion(
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "image.jpg"}},
            {"type": "text", "text": "What's in this image?"}
        ]
    }]
)
print(output["choices"][0]["message"]["content"])

Quantization Details

Source: zai-org/GLM-4.6V-Flash → F16 GGUF → llama-quantize.exe NVFP4
Block size: 64 elements; Per-Group UE4M3 scales (4 scales per block)
Output tensor: Q6_K (higher precision for the final projection)
Architecture: glm4 with 523 tensors (40 transformer layers, vision embedder)

Hardware Compatibility

Requires NVIDIA Blackwell (RTX 50 series) for native FP4 compute via CUDA Blackwall
Falls back to FP4 dequantization on older GPUs (slower but functional)
CPU inference supported via software dequant (significantly slower)

FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF

GLM-4.6V-Flash NVFP4 GGUF

Model Details

Files

Usage

LM Studio

llama.cpp

Python (llama-cpp-python)

Quantization Details

Hardware Compatibility

FreedomAISVR/
GLM-4.6V-Flash-NVFP4-GGUF