Multi-modal Inference

Basic idea

Multi-modal inference combines different data types, such as text, images, and audio, within a single model pipeline to generate or interpret richer outputs.
Instead of processing one input type at a time, multi-modal systems have shared representations that text-to-image generation, image captioning, or vision-language reasoning.

On GPUs, this enables parallel processing across modalities for faster, higher-fidelity results for tasks that combine language and vision.

What you'll accomplish

You'll deploy GPU-accelerated multi-modal inference capabilities on NVIDIA Spark using TensorRT to run Flux.1 and SDXL diffusion models with optimized performance across multiple precision formats (FP16, FP8, FP4).

What to know before starting

• Working with Docker containers and GPU passthrough
• Using TensorRT for model optimization
• Hugging Face model hub authentication and downloads
• Command-line tools for GPU workloads
• Basic understanding of diffusion models and image generation

Prerequisites

• NVIDIA Spark device with Blackwell GPU architecture
• Docker installed and accessible to current user
• NVIDIA Container Runtime configured
• Hugging Face account with access to Black Forest Labs models FLUX.1-dev and FLUX.1-dev-onnx on Hugging Face
• Hugging Face token configured with access to both FLUX.1 model repositories
• At least 48GB VRAM available for FP16 Flux.1 Schnell operations
• Verify GPU access: nvidia-smi
• Check Docker GPU integration: docker run --rm --gpus all nvcr.io/nvidia/pytorch:25.11-py3 nvidia-smi

Ancillary files

All necessary files can be found in the TensorRT repository here on GitHub

• requirements.txt - Python dependencies for TensorRT demo environment
• demo_txt2img_flux.py - Flux.1 model inference script
• demo_txt2img_xl.py - SDXL model inference script
• TensorRT repository - Contains diffusion demo code and optimization tools

Time & risk

• Duration: 45-90 minutes depending on model downloads and optimization steps

• Risks:

  • Large model downloads may timeout
  • High VRAM requirements may cause OOM errors
  • Quantized models may show quality degradation

• Rollback:

  • Remove downloaded models from HuggingFace cache
  • Then exit the container environment

• Last Updated: 12/22/2025
  • Upgrade to latest pytorch container version nvcr.io/nvidia/pytorch:25.11-py3
  • Add HuggingFace token setup instructions for model access
  • Add docker container permission setup instructioins