The KV Cache: Memory Usage in Transformers
Try Voice Writer - speak your thoughts and let AI handle the grammar: https://voicewriter.io The KV cache is what takes up the bulk of the GPU memory during inference for large language models like GPT-4. Learn about how the KV cache works in this video! 0:00 - Introduction 1:15 - Review of self-attention 4:07 - How the KV cache works 5:55 - Memory usage and example Further reading: Speeding up the GPT - KV cache (https://www.dipkumar.dev/becoming-the...) Transformer Inference Arithmetic (https://kipp.ly/transformer-inference...) Efficiently Scaling Transformer Inference (https://arxiv.org/pdf/2211.05102.pdf)

▶︎
Rotary Positional Embeddings: Combining Absolute and Relative

▶︎
Attention in transformers, step-by-step | Deep Learning Chapter 6

▶︎
KV Cache in LLM Inference - Complete Technical Deep Dive

▶︎
KV Cache in 15 min

▶︎
PagedAttention: Behind vLLM's Insane Speed

▶︎
KV Cache: The Invisible Trick Behind Every LLM

▶︎
Fast LLM Serving with vLLM and PagedAttention

▶︎
Why Inference is hard..

▶︎
KV Cache in LLMs Explained Visually | How LLMs Generate Tokens Faster

▶︎
They solved AI’s memory problem!

▶︎
KV Cache Crash Course

▶︎
KV Cache Explained

▶︎
Understanding vLLM with a Hands On Demo

▶︎
Most devs don't understand how LLM tokens work

▶︎
FlashAttention: Accelerate LLM training

▶︎
Self-Attention Explained: How Transformers Actually Work (Full Visual Breakdown)
![KV Caching: Speeding up LLM Inference [Lecture]](https://i.ytimg.com/vi/_quDGLpNols/hqdefault.jpg?sqp=-oaymwE9CNACELwBSFryq4qpAy8IARUAAAAAGAElAADIQj0AgKJDeAHwAQH4Af4JgALQBYoCDAgAEAEYciA-KEowDw==&rs=AOn4CLDNjwLJ14YISrwLD_X3VgOOto3_ag)
▶︎
KV Caching: Speeding up LLM Inference [Lecture]

▶︎
How vLLM Became the Standard for Fast AI Inference | Simon Mo, Inferact

▶︎
What is vLLM? Efficient AI Inference for Large Language Models

▶︎
