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container-llm-cuda-optimization

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GPU-optimized LLM Docker containers.

Software Engineering#llm#docker#gpu#pytorch#cuda#image-optimization#multistage-build
Authorjfriisj
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Expert guidance for building highly optimized, GPU-accelerated Docker containers for LLMs and CUDA, helping prevent image bloat and ensure correct runtime vs devel configurations across development to production.

Core Features & Use Cases

  • Nvidia Base Image Strategy: Use the correct base image tier (base, runtime, devel) to minimize image size while providing required libraries.
  • Multi-Stage CUDA Architecture: Build complex CUDA extensions in a devel stage and copy built artifacts into a lean runtime stage.
  • Dependency Management: Explicitly pin PyTorch CUDA flavor via the right index URL to avoid mixing CPU and GPU wheels.
  • Weights Handling Guidance: Avoid baking 20+ GB weights into images; mount weights at runtime or download to a shared volume.

Quick Start

Apply the multi-stage CUDA Docker strategy to build a lean runtime image for deploying LLM workloads.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

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Please help me install this Skill:
Name: container-llm-cuda-optimization
Download link: https://github.com/jfriisj/coding-agents/archive/main.zip#container-llm-cuda-optimization

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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