Build Your Own LLM Server in a Week

Master LLM Serving in Just One Week with Tiny LLM

For systems engineers eager to unravel the intricacies of Large Language Models (LLMs), a groundbreaking open-source project, 'Tiny LLM', offers a unique, intensive learning experience. Designed to demystify LLM serving, this practical course guides participants through building and optimizing an LLM inference system within a single week.

What is Tiny LLM?

Tiny LLM is an ambitious initiative aimed at making LLM internals accessible to systems engineers. Recognizing the complexity of highly optimized, low-level LLM serving codebases, the creators have developed a course that starts with fundamental matrix manipulation APIs. This approach allows learners to grasp the core concepts of loading model parameters and performing the mathematical operations essential for text generation, akin to the CMU Deep Learning Systems course's 'needle' project.

Course Structure and Prerequisites

The course is structured over three weeks, focusing on serving and optimizing the Qwen2-7B-Instruct model.

• Week 1: Build a functional LLM server using pure Python and matrix manipulation APIs.
• Week 2: Enhance performance by implementing C++/Metal custom kernels.
• Week 3: Further optimize throughput by exploring request batching.

Ideal for individuals with a foundational understanding of deep learning and familiarity with PyTorch, the course leverages MLX, an array and machine learning library optimized for Apple Silicon. While theoretical compatibility with PyTorch or NumPy exists, MLX is the primary tested environment, ensuring a smooth learning curve.

A Guidebook Approach

Tiny LLM is presented as a guidebook rather than a traditional textbook. It curates and unifies useful online resources, providing clear task lists and essential hints. This method focuses on practical application, leaving in-depth concept explanations to the vast resources available online, while ensuring consistency in terminology and tensor dimension notation for seamless code integration.

Built by Experts, for the Community

Created by Chi (Systems Software Engineer at Neon/Databricks) and Connor (Software Engineer at PingCAP), Tiny LLM stems from a desire to understand LLM inference deeply. They aim to provide the community with practical, hands-on experience in building high-performance LLM serving systems.

Get Started

Ready to embark on this educational journey? Begin by setting up your environment following the provided instructions and dive into building your own Tiny LLM. The project encourages community participation and feedback, welcoming contributions via GitHub and discussions on their Discord server. Join the growing community of learners and developers shaping the future of LLM deployment.