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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: Managing Python on macOS with uv

Quick Start

# Install uv
brew install uv

# For new projects (modern workflow)
uv init                # create project structure
uv add pandas numpy    # add dependencies
uv run train.py        # run your script

# For existing projects (legacy workflow)
uv venv                             # create virtual environment
uv pip install -r requirements.txt  # install dependencies
uv run train.py                     # run your script

# Run tools without installing them
uvx ruff check .       # run linter
uvx black .            # run formatter