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Scaling Large Language Models - Practical Multi-GPU and Multi-Node Strategies for 2025

The race to build bigger, better language models continues at breakneck speed. Today's state-of-the-art models require massive computing resources that no single GPU can handle. Whether you're training a custom LLM or deploying one for inference, understanding how to distribute this workload is essential.

This guide walks through practical strategies for scaling LLMs across multiple GPUs and nodes, incorporating insights from Hugging Face's Ultra-Scale Playbook.

Quick-Guide on managing Python like an AI Engineer on macOS with uv

TL;DR Bash Cheat‑sheet

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brew install uv        # install tool
uv python install 3.12 # grab interpreter

# New project workflow (modern)
uv init                # create new project with pyproject.toml
uv add pandas numpy    # add dependencies
uv run train.py        # run with correct interpreter

# Classical project workflow (requirements.txt)
uv venv                           # create .venv
uv pip install -r requirements.txt # install from requirements
uv run train.py                   # run script

brew upgrade uv         # update uv itself (Homebrew install)