LLM Serving with vLLM
Self-host a Llama or Qwen model with vLLM, expose an OpenAI-compatible API, and benchmark it.
vLLMPythonCUDADockerHugging Face Hub
About this project
Self-hosting open-source models is the cost-control specialty in 2026. This project teaches vLLM (the dominant high-throughput serving engine), continuous batching, KV cache management, and the operational details. Spin up an instance (RunPod, Lambda Labs), serve a model, expose an OpenAI-compatible endpoint, and write the benchmark.
Why build this in 2026?
Cost-aware ML serving is the highest-leverage ML eng skill — switching from GPT-4o to a self-hosted Llama can cut costs 10x.
What you'll ship
- GitHub repo
Benchmark report (TTFT, throughput, cost per 1M tokens)
Writeup comparing to OpenAI API
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Skills you'll practice
pythonmachine learningdockerrest apis