Phase 8: Serverless GPU Deployment & Infrastructure · 55 min · Python · Docker · NVIDIA Container Toolkit

Docker Packaging for GPU Models

Docker + CUDA base image + model weights + inference script = reproducible GPU deployment.

Hiring signal: Docker packaging for GPU models (CUDA base images, model weight management, multi-stage builds) is a core infrastructure skill for generative media deployment roles.

What you will learn

Create Docker images for GPU models: nvidia-runtime, CUDA base images, model file management
Package FLUX.1 Schnell: base image + model weights + inference script
Package ComfyUI: ComfyUI + custom nodes + models + handler script
Optimize image size: multi-stage builds, model weight layering, squash

The Problem

A team needs to deploy a FLUX.1 Schnell model to RunPod Serverless. They need:

A Docker image with CUDA, PyTorch, Diffusers, and model weights
An inference script that receives jobs and returns generated images
Optimized image size (model weights are 20+ GB)
Reproducible builds (same Dockerfile → same image)

Docker packaging for GPU models is a core infrastructure skill for generative media deployment.

What you'll build
Create Docker images for GPU models using NVIDIA CUDA base images, package FLUX.1 Schnell and ComfyUI, optimize image size with multi-stage builds, and write handler scripts for serverless deployment.

NVIDIA CUDA Base Images

NVIDIA provides official CUDA Docker images via NVIDIA Container Toolkit:

# Base image options:
# nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04  — runtime only (smaller)
# nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04    — development (larger, includes compiler)
# nvidia/cuda:12.4.1-base-ubuntu22.04            — base (smallest, no cuDNN)

FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04

Image Selection Guide

Image	Size	Use Case
`base`	~3GB	Minimal CUDA, install cuDNN yourself
`runtime`	~5GB	CUDA + cuDNN, best for deployment
`devel`	~7GB	CUDA + cuDNN + compiler, for building

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Packaging FLUX.1 Schnell, Packaging ComfyUI, Multi-Stage Build Optimization, Key Takeaways, What's Next — plus a hands-on lab, quiz, and project artifact.

Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.

Key terms in this lesson

Inference