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April 27, 2026Open Access

From Attention to Inference - A Technical Study of Large Language Models

Key Points

This paper aims to provide a detailed technical overview of the Transformer architecture used in Natural Language Processing.
Examines core components of Transformer architecture such as self-attention, tokenization, and positional encoding.
Discusses model families including encoder-only, decoder-only, and encoder-decoder.
Analyzes system-level considerations like KV caching, throughput, and VRAM usage.
Presents a structured reference for practitioners and students in machine learning and NLP.
Highlights the importance of pretraining and fine-tuning for effective inference.
Discusses metrics like Time to First Token as critical for model performance.

Abstract

This paper presents a technical overview of the Transformer architecture and its role in modern Natural Language Processing (NLP). It examines the core components of the paradigm including self-attention mechanisms, tokenization, positional encoding, model families (encoder-only, decoder-only, and encoder–decoder), pretraining objectives, fine-tuning, and inference processes. System-level considerations such as KV caching, Time to First Token, throughput, and VRAM usage are also discussed. The paper is intended as a structured technical reference for practitioners and students working in machine learning and NLP.

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THOMAS SIOUMPALAS (Sat,) studied this question.

synapsesocial.com/papers/69eefd43fede9185760d3fd0 https://doi.org/https://doi.org/10.5281/zenodo.19762382

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