Demystifying the Core Technologies Behind ChatGPT, GPT‑4, and DeepSeek

This article breaks down the key algorithms that power large‑language models—Transformer, Mixture‑of‑Experts, Flash Attention, KV‑Cache, Multi‑Token Prediction, quantization, Chain‑of‑Thought and Retrieval‑Augmented Generation—explaining how each contributes to the performance of ChatGPT, GPT‑4 and DeepSeek.

Flash AttentionKV CacheMixture of Experts

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Demystifying the Core Technologies Behind ChatGPT, GPT‑4, and DeepSeek

Geek Labs

Apr 20, 2026 · Artificial Intelligence

A Complete Open‑Source Guide to LLM Internals: From Tokenization to Inference Optimization

This open‑source tutorial breaks down large language model internals into 11 detailed topics—covering BPE tokenization, attention mathematics, backpropagation, transformer architecture, KV‑Cache, Paged and Flash Attention, and frontier techniques—each with numeric derivations and Python code, making it ideal for developers and interview preparation.

Flash AttentionInference OptimizationKV Cache

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A Complete Open‑Source Guide to LLM Internals: From Tokenization to Inference Optimization

AI Frontier Lectures

Jun 3, 2025 · Artificial Intelligence

Master LLM Engineering: Model Conversion, Parallel Inference, and Channel‑Loss Techniques

This article outlines essential LLM engineering skills, including scripts for converting various model checkpoints to Llama format, customizing modeling files for advanced features, building a multi‑GPU inference class, and adding channel‑aware loss tracking to fine‑tuning pipelines.

Flash AttentionLLMTraining Optimization

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Master LLM Engineering: Model Conversion, Parallel Inference, and Channel‑Loss Techniques

NewBeeNLP

Oct 16, 2024 · Artificial Intelligence

Unlocking Long-Sequence LLMs: Position Embeddings, Scaling, and Efficient Attention

This article reviews recent advances in training and inference for long‑sequence large language models, comparing ALIBI and RoPE position embeddings, exploring RoPE scaling techniques, analyzing attention optimizations, and outlining practical data, evaluation, and system frameworks for scalable LLM deployment.

Flash AttentionLLMRoPE

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Unlocking Long-Sequence LLMs: Position Embeddings, Scaling, and Efficient Attention

Baobao Algorithm Notes

Oct 19, 2023 · Artificial Intelligence

Efficient LLM Deployment: Low‑Precision, Flash Attention, and Architecture Tricks

This article reviews the main memory and compute challenges of deploying large language models and presents practical solutions—including low‑precision arithmetic, flash attention, advanced positional embeddings, key‑value caching, and quantization techniques—backed by code examples and performance measurements on models such as OctoCoder.

Flash AttentionLLMTransformers

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Efficient LLM Deployment: Low‑Precision, Flash Attention, and Architecture Tricks