The Complete Guide to How LLMs Work and What Makes Each Model Different

2026-07-13 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, extended

Summary

The article provides a comprehensive guide to Large Language Models (LLMs), detailing their fundamental operation and the architectural distinctions among leading models. It explains the Transformer architecture, including tokenization, embedding, multi-head attention, positional encoding (RoPE), feed-forward networks, and normalization. The two-phase training process, pre-training and post-training (SFT, RLHF, Constitutional AI), is outlined. Advanced optimizations like Grouped Query Attention (GQA), Multi-Head Latent Attention (MLA), Compressed Sequence Attention (CSA/HCA), Mixture of Experts (MoE), YaRN, Speculative Decoding, and Quantization are described, primarily focusing on memory and compute efficiency for long contexts. The guide then compares specific models like DeepSeek V4, Kimi K2.6, Claude Opus 4.7, GPT-5.5, Qwen 3.6, Gemma 4, Llama 4, Mistral Large 3, Grok 3, Command R+, and Gemini 2.5 Pro, highlighting their unique innovations in areas such as KV cache compression, agentic scaling, alignment, dynamic reasoning, and multimodal integration.

Key takeaway

For AI Architects and Machine Learning Engineers evaluating LLM deployments, understanding the diverse architectural innovations is critical for optimizing performance and cost. You should prioritize models based on your specific needs: DeepSeek V4 or Llama 4 Scout for extreme context, MoE models like Qwen 3.6 or Mistral Large 3 for cost-efficient scale, or Claude Opus 4.7 for high-stakes, low-hallucination applications. Consider Gemma 4 for on-device multimodal capabilities or Command R+ for RAG with native citations.

Key insights

LLM innovation extends beyond parameter count, focusing on architectural efficiency, reasoning control, and multimodal integration for diverse applications.

Principles

• KV cache compression is crucial for scaling LLMs to ultra-long contexts.
• Mixture of Experts (MoE) enables vast knowledge capacity with efficient inference.
• Training methodology significantly impacts model alignment and hallucination rates.

Method

The Transformer forward pass involves tokenization, embedding, positional encoding, repeated layers of attention and feed-forward networks, followed by output prediction.

In practice

• Implement GQA or MLA to reduce KV cache memory for longer sequences.
• Use MoE models like Qwen 3.6 for cost-efficient, high-capacity inference.
• Apply YaRN to extend context windows of pre-trained models without retraining.

Topics

• LLM Architectures
• Transformer Models
• KV Cache Optimization
• Mixture of Experts
• Multimodal AI
• LLM Training & Alignment
• Positional Encoding

Best for: AI Scientist, Machine Learning Engineer, AI Architect

Related on AIssential

• LLMs: A Journey Through Time and Architecture — LLM evolution prioritizes data quality, multi-stage pre-training, and architectural efficiency over sheer model size.
• The KV-Cache of Small MoEs: Qwen3, Qwen3.5, GLM 4.7 Flash, and Nemotron 3 Nano Compared — MoE models achieve inference efficiency through diverse attention architectures and KV cache optimization strategies.
• LLM Building Blocks & Transformer Alternatives — Advanced transformer architectures and emerging alternatives aim to balance model scale, inference efficiency, and specialized capabilities.
• Chinese AI Models Are Taking Over US Industry — Open-source Chinese AI models now rival or exceed Western counterparts in performance and accessibility across various tasks.
• Reiner Pope – The math behind how LLMs are trained and served — LLM inference and training costs are fundamentally dictated by hardware architecture, particularly memory bandwidth and batch size optimizations.
• Intro to Mixture of Experts | Aritra Roy Gosthipaty | HF Podcast #2 — Mixture of Experts (MOEs) enhance LLM efficiency by sparsely activating parameters, making them ideal for large foundational models.

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.