README.md

metadata

language:
  - en
  - zh
tags:
  - fp8
  - quantization
  - dynamic
  - vision-language
  - multimodal
  - vllm
  - llm-compressor
  - internvl3.5
base_model: OpenGVLab/InternVL3_5-1B
base_model_relation: quantized
pipeline_tag: image-text-to-text
inference: false
license: mit

🔥 InternVL3_5-1B-FP8-Dynamic 🔥

This is a fp8 dynamic (w8a8) version of OpenGVLab/InternVL3_5-1B, optimized for high-performance inference with vLLM. The model utilizes fp8 dynamic (w8a8) for optimal performance and deployment.

Just Run It (vLLM serve)

You can serve the model using vLLM's OpenAI-compatible API server.

vllm serve brandonbeiler/InternVL3_5-1B-FP8-Dynamic \
    --quantization compressed-tensors \
    --served-model-name internvl3_5-1b \
    --reasoning-parser qwen3 \
    --trust-remote-code \
    --max-model-len 32768 \
    --tensor-parallel-size 1 # Adjust based on your GPU setup

Notes

• 32k max context length
• reasoning parser ready to go, requires system prompt to run in thinking mode
• still investigating tool calling

🚀 Key Features

• FP8 Dynamic Quantization: No calibration required, ready to use immediately
• 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_5-1B
• Source Model: OpenGVLab/InternVL3_5-1B
• Quantized Model: InternVL3_5-1B-FP8-Dynamic
• Quantization Method: FP8 Dynamic (W8A8)
• Quantization Library: LLM Compressor v0.7.1
• Quantized by: brandonbeiler

🏗️ Technical Specifications

Hardware Requirements

• Inference: ? VRAM (+ VRAM for context)
• Supported GPUs: H100, L40S, A100 (80GB), RTX 4090 (2x for tensor parallelism)
• GPU Architecture: Latest NVIDIA GPUs (Ada Lovelace, Hopper and later) and latest AMD GPUs. Recommended for NVIDIA GPUs with compute capability >=9.0 (Hopper and Blackwell)

Quantization Details

• Weights: FP8 E4M3 with dynamic per-tensor scales
• Activations: FP8 E4M3 with dynamic per-tensor scales
• Preserved Components: Vision tower, embeddings, mlp1

🔬 Package Versions

This model was created using:

llmcompressor==0.7.1
compressed-tensors==0.10.2
transformers==4.55.0
torch==2.7.1
vllm==0.10.1.1

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