PagedAttention

A visualization of memory blocks and tokens in a neural network inference engine

Building a Mini LLM Inference Engine in Pure Python

A hands-on guide to building a mini LLM inference engine featuring PagedAttention and advanced KV cache management, designed to demonstrate production-grade systems architecture on your CV.

Code editor showing a Python-based LLM serving engine implementation

Build a Pure-Python Continuous Batching LLM Serving Engine from Scratch

A hands-on guide to building a pure-Python continuous batching LLM serving engine with PagedAttention KV cache and dynamic batch scheduling — a portfolio project that signals deep systems engineering skill.

Diagram of vLLM's PagedAttention KV cache allocator mapping logical blocks to physical GPU memory pages.

Optimizing vLLM's PagedAttention Allocator for High-Throughput Batched Inference

A working engineer’s guide to the internals of vLLM’s PagedAttention memory allocator, with concrete knobs, profile traces, and production patterns for squeezing more tokens per second out of a single GPU.

Zero to Hero with vLLM: A Practical Guide for High‑Throughput LLM Inference

Introduction If you’re trying to serve large language models (LLMs) efficiently on GPUs, you quickly run into a wall: GPU memory gets eaten by KV cache Throughput collapses as concurrent users increase You spend more on hardware than on your actual application vLLM is an open-source inference engine designed to fix this. It combines: A highly optimized attention implementation (PagedAttention) Continuous batching and scheduling A production-ready API server (OpenAI-compatible) Tight GPU memory management This tutorial is a concise zero-to-hero guide for developers who want to: ...