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Inference Engines — A visual deep dive into the journey of a token down the transformer layers

Reddit r/LocalLLaMA / 3/29/2026

💬 OpinionIdeas & Deep AnalysisTools & Practical Usage
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Key Points

  • The author introduces a beginner-friendly, part 1 visual deep dive focused on what happens when a token passes through transformer layers during inference.
  • The post is motivated by building an inference engine (inspired by Ollama) and then seeking deeper understanding of internal behavior to better evaluate and interpret optimizations.
  • It emphasizes learning the underlying mechanics so readers can tell why certain optimization attempts may not produce the expected results.
  • The article frames the content as a staged exploration (“part 1”), with the goal of helping readers build accurate intuition about transformer inference.

I spent a lot of time building an inference engine like ollama, pure vibe coding in go. I kept trying to push it to optimize it and it was fun but after sometime I really wanted to know what was going on to be able to really know what those optimizations were about and why some were'nt working as I expected. This is a part 1 of those articles that go deep and is beginner friendly to get up to speed with inference.

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