A course on LLM serving using MLX for system engineers. The codebase is solely (almost!) based on MLX array/matrix APIs without any high-level neural network APIs, so that we can build the model serving infrastructure from scratch and dig into the optimizations.

The goal is to learn the techniques behind efficiently serving a large language model (e.g., Qwen2 models).

In week 1, you will implement the necessary components in Python (only Python!) to use the Qwen2 model to generate responses (e.g., attention, RoPE, etc). In week 2, you will implement the inference system which is similar to but a much simpler version of vLLM (e.g., KV cache, continuous batching, flash attention, etc). In week 3, we will cover more advanced topics and how the model interacts with the outside world.

Why MLX: nowadays it's easier to get a macOS-based local development environment than setting up an NVIDIA GPU.

Why Qwen2: this was the first LLM I've interacted with -- it's the go-to example in the vllm documentation. I spent some time looking at the vllm source code and built some knowledge around it.

The tiny-llm book is available at https://skyzh.github.io/tiny-llm/. You can follow the guide and start building.

You may join skyzh's Discord server and study with the tiny-llm community.

Week 1 is complete. Week 2 is in progress.

Other topics not covered: quantized/compressed kv cache, prefix/prompt cache; sampling, fine tuning; smaller kernels (softmax, silu, etc)