Building an LLM Inference Engine from Scratch
Building an LLM Inference Engine from Scratch I recently started exploring machine learning and AI from a systems engineering perspective. While most people begin by focusing on model training, I found myself much more interested in the inference side of the stack. I wanted to understand how deep learning models and neural networks work under the hood, but what fascinated me even more were the engineering breakthroughs that made modern LLM serving possible. Everywhere I looked, people were discussing innovations such as PagedAttention in vLLM, RadixAttention in SGLang, continuous batching, speculative decoding, fused attention kernels, and distributed inference. ...