Lakshmanan LN

Lakshmanan LN

Hi, I’m Lakshmanan LN — an AI/ML engineer working on GenAI and RAG systems, MLOps, and ML architecture, writing about the systems layer underneath all of it: attention internals, LLM inference engines, and the training dynamics behind why models behave the way they do.

This blog is where I work through those topics properly — first principles first, code and diagrams over hand-waving, honest about where a technique breaks down.

Connect with me on LinkedIn, X, or YouTube to talk shop or compare notes.

vLLM Internals: PagedAttention, Continuous Batching, and Where the 2-4x Comes From

vLLM Internals: PagedAttention, Continuous Batching, and Where the 2-4x Comes From

A decode step moves every weight in the model from HBM into the SMs to produce exactly one token per sequence. At batch size 1 that is a …

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Scaling Laws: Kaplan, Chinchilla, and Why Nobody Trains Chinchilla-Optimal

Scaling Laws: Kaplan, Chinchilla, and Why Nobody Trains Chinchilla-Optimal

Say you’ve got loss numbers from a 40M-parameter run and a 400M-parameter run, same data, same architecture family, same optimizer. …

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Attention From Scratch: How Transformers Replaced a Bottleneck with a Lookup

Attention From Scratch: How Transformers Replaced a Bottleneck with a Lookup

Take a vanilla encoder-decoder RNN doing machine translation. It reads a source sentence token by token, updating one hidden-state vector as …

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