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What you can do with LLM Inference Engineering

A free, step-by-step learning series of blog posts and videos on LLM inference engineering, covering topics such as KV cache, PagedAttention, continuous batching, and speculative decoding.

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amitshekhariitbhu/llm-inference-engineering on GitHub

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What do you want to do?

4 use cases

  • Learn how LLMs generate text one token at a time

    Work through the Part 1 basics on autoregressive generation and the prefill and decode phases.

    View prompt
    Using the amitshekhariitbhu/llm-inference-engineering repo, read the Part 1 section on <topic> and produce a one-page summary of its key terms. Verify that each term in the summary is defined in the linked Outcome School post.

  • Pick a KV cache memory technique for long prompts

    Compare KV cache compression, PagedAttention, GQA, and Flash Attention to choose a memory approach.

    View prompt
    Using the amitshekhariitbhu/llm-inference-engineering repo, compare the KV cache techniques for the workload <workload description> and output a table with one row per technique. Verify that each row matches the 'Comparison of the approaches' and 'When to use which one' sections of the KV Cache Compression post.

  • Choose a faster decoding method for slow responses

    Compare speculative decoding, Medusa, EAGLE, and n-gram speculation to pick one to study.

    View prompt
    Using the amitshekhariitbhu/llm-inference-engineering repo, compare the speculative decoding methods for the case <case description> and return a ranked list with requirement and trade-off for each. Verify that every claim about speedup in the list is taken from the linked blog posts.

  • Understand how LLM servers handle many users at once

    Read about continuous batching, streaming, and disaggregated serving to plan an LLM API.

    View prompt
    Using the amitshekhariitbhu/llm-inference-engineering repo, draft a one-page design note for an LLM API serving <number> concurrent users. Verify that the note explains continuous batching, streaming, and disaggregation using only the linked posts.

How to use LLM Inference Engineering

  1. Check you have what it needs: A web browser to open the linked blog posts; Basic familiarity with how text generation works, or willingness to start with the Part 1 basics.
  2. Open amitshekhariitbhu/llm-inference-engineering on GitHub and read the README and license.
  3. Open the Autoregressive Models post at https://outcomeschool.com/blog/autoregressive-models and read the generation loop and the numeric example.
  4. Open the Prefill vs Decode post at https://outcomeschool.com/blog/prefill-vs-decode-llm-inference-optimization and read the comparison table.
  5. Read the key metrics section on TTFT, TPOT, throughput, and end-to-end latency in the same post.
  6. Write a short summary of the difference between prefill and decode in your own words.

These steps come from our top use case, Learn how LLMs generate text one token at a time, which also has a copy-paste agent prompt.

LLM Inference Engineering alternatives · Best AI repos for Research

About this repo

Requirements

  • A web browser to open the linked blog posts
  • Basic familiarity with how text generation works, or willingness to start with the Part 1 basics
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Questions about LLM Inference Engineering

What is LLM Inference Engineering?

A free, step-by-step learning series of blog posts and videos on LLM inference engineering, covering topics such as KV cache, PagedAttention, continuous batching, and speculative decoding. It is an open-source project on GitHub: https://github.com/amitshekhariitbhu/llm-inference-engineering.

Who is LLM Inference Engineering for?

Software engineers and ML practitioners who need to understand how LLMs are served and sped up before they build or tune an inference system.

What can I do with LLM Inference Engineering?

4 use cases so far, for example: Learn how LLMs generate text one token at a time; Pick a KV cache memory technique for long prompts; Choose a faster decoding method for slow responses. Each has steps and a copy-paste agent prompt.

What do I need to run LLM Inference Engineering?

A web browser to open the linked blog posts; Basic familiarity with how text generation works, or willingness to start with the Part 1 basics.

What are alternatives to LLM Inference Engineering?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is LLM Inference Engineering safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.