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How to compare LLM serving tools before picking one with AI Engineering Course

Use the inference module to compare the serving tools the README names.

  • Research
  • Intermediate
  • 30 minutes
  • From the README
  • Any agent

Who it is forAn engineer deciding how to serve an LLM and who needs a map of the options before testing any of them.

The problemServing tools differ in batching, memory use, and hardware support, and the choice is hard to make without a framework for comparing them.

The outcomeA comparison note that lists each serving tool named in the README with the inference concepts it relates to.

What you need first

  • A web browser to open the GitHub repo and read the lessons
  • Basic programming knowledge, preferably Python, for the code examples
  • Time to read lessons in order, module by module

Steps

  1. Open Module 12: LLM Inference Engineering in the repo README.
  2. Read the list of topics: prefill vs decode, KV cache, paged attention, continuous batching, and quantization.
  3. List the tools named in the course: llama.cpp, Ollama, vLLM, SGLang, and TensorRT-LLM.
  4. For each tool, write which of the inference topics it is linked to in the course.
  5. Open the lesson for each topic and check the claims before you use the note in a decision.

Prompt for your agent

From the README of amitshekhariitbhu/ai-engineering-course, extract the inference topics in Module 12 and the tools it names, then build a table for <target workload>. Output the table with topic, tool, and the lesson name, and verify that every tool appears in the README text.

Caveat. The README names these tools but does not compare their performance, so the table only shows which topics the course links to each tool and does not rank them.

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