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LLM Decode Explained
Understanding LLM Inference | NVIDIA Experts Deconstruct How AI Works
Inside LLM Inference: GPUs, KV Cache, and Token Generation
Large Language Models explained briefly
LLM Inference Explained: 12 Concepts You Actually Need to Know
Why LLMs Read Fast but Write Slowly - Prefill vs Decode
AI Infrastructure Explained (GPUs, vLLM, and LLM-D)
Structured Output from LLMs: Grammars, Regex, and State Machines
Why Separating Prefill and Decode Makes LLMs Faster | vLLM, LLM-D and NIXL
How KV Cache Speeds Up LLMs for Faster AI Models on GPUs
Deep Dive into LLMs like ChatGPT
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Last Updated: September 26, 2026
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Summary
Most devs are using LLMs daily but don't have a clue about some of the fundamentals. Understanding tokens is crucial because ... In this video, we break down the two fundamental stages of Why does your GPU hit 100% utilization during prefill... then suddenly drop to 20% during generation? Because Prefill and ... Every time you send a message to ChatGPT, Claude, or Gemini — two completely different machines now handle your request. In the last eighteen months, large language models (LLMs) have become commonplace. For many people, simply being able to ... A light intro to LLMs, chatbots, pretraining, and transformers. Dig deeper here: ... Inference engines are full of jargon — continuous batching, paged attention, prefix caching, speculative LLMs can process a long prompt quickly, but generating a long answer takes much more time. Because Try it yourself in the free lab: kode. Try Voice Writer - speak your thoughts and let AI handle the grammar: voicewriter.io Structured outputs are essential for ... 00:00 Introduction & Why Prefill/ This is a general audience deep dive into the Large Language Model (