---
language:
- en
tags:
- fp8
- quantization
- dynamic
- vision-language
- multimodal
- vLLM
- llm-compressor
- internvl3
pipeline_tag: image-text-to-text
inference: false
license: mit
base_model:
- OpenGVLab/InternVL3-78B
---
# 🔥 InternVL3-78B-FP8-Dynamic: Optimized Vision-Language Model 🔥
This is a **FP8 dynamic quantized** version of [OpenGVLab/InternVL3-78B](https://huggingface.co/OpenGVLab/InternVL3-78B), optimized for high-performance inference with vLLM. 
The model utilizes **dynamic FP8 quantization** for optimal ease of use and deployment, achieving significant speedup with minimal accuracy degradation on vision-language tasks.

## 🔧 Usage
### With vLLM (Recommended)
```python
from vllm import LLM, SamplingParams

# Load the quantized model
model = LLM(
    model="brandonbeiler/InternVL3-78B-FP8-Dynamic",
    trust_remote_code=True,
    max_model_len=8192,
    tensor_parallel_size=1,  # Adjust based on your GPU setup
)
# Generate response
sampling_params = SamplingParams(temperature=0.7, max_tokens=512)
response = model.generate("Describe this image: <image>", sampling_params)
print(response[0].outputs[0].text)
```

## 🚀 Key Features
- **FP8 Dynamic Quantization**
- **Vision-Language Optimized**: Specialized quantization recipe that preserves visual understanding
- **vLLM Ready**: Seamless integration with vLLM for production deployment  
- **Memory Efficient**: ~50% memory reduction compared to FP16 original
- **Performance Boost**: Significant faster inference on H100/L40S GPUs
## 📊 Model Details
- **Original Model**: [OpenGVLab/InternVL3-78B](https://huggingface.co/OpenGVLab/InternVL3-78B)
- **Source Model**: OpenGVLab/InternVL3-78B
- **Quantized Model**: InternVL3-78B-FP8-Dynamic
- **Quantization Method**: FP8 Dynamic (W8A8) 
- **Quantization Library**: [LLM Compressor](https://github.com/vllm-project/llm-compressor) v0.5.2.dev110+gf6010ce1
- **Quantized by**: [brandonbeiler](https://huggingface.co/brandonbeiler)

## 🏗️ Technical Specifications
### Hardware Requirements
- **Inference**: 83 VRAM (+ VRAM for context)
- **Supported GPUs**: H100, L40S, A100 (80GB), RTX 4090 (2x for tensor parallelism)
- **GPU Architecture**: Ada Lovelace, Hopper (for optimal FP8 performance)
### Quantization Details
- **Weights**: FP8 E4M3 with dynamic per-tensor scales
- **Activations**: FP8 E4M3 with dynamic per-tensor scales  
- **Preserved Components**: Vision tower, embeddings, normalization layers, mlp1
## 🔬 Package Versions
This model was created using:
```
llmcompressor==0.5.2.dev110+gf6010ce1
compressed-tensors==latest
transformers==4.52.4
torch==2.7.0
vllm==0.9.1
```

*Quantized with ❤️ using LLM Compressor for the open-source community*