The Architecture Behind Serving LLMs to Millions

Try Crusoe with free credits: https://fandf.co/4fXYv1g And thanks to Crusoe for sponsoring this video ❤️ ======⚡⚡⚡====== Learn how to scale a large language model (LLM) from one user to one million users in this deep dive into LLM serving, AI infrastructure, and production machine learning system, covering continuous batching, KV cache and memory management, tensor parallelism vs. pipeline parallelism, multi-LoRA serving, and GPU cluster deployment with Kubernetes and KServe. Consider becoming a member of the channel by joining me ❤️ https://www.youtube.com/channel/UCqAL_b-jUOTPjrTSNl2SNaQ/join ☕ Buy me a coffee: https://www.buymeacoffee.com/softwaredeveloperdiaries 🙌 Become my Patreon and get exclusive perks: https://patreon.com/softdevdiaries 💼 Follow me on LinkedIn and drop me a message if you'd like: https://www.linkedin.com/in/gadirovgs/ 💻 Also, let's connect on GitHub: https://github.com/gusgad 📚 Resources: Tips for fine-tuning LLMs: https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms 🕒 Timestamps: 0:00 Stage 1 3:51 Stage 2 6:12 Stage 3 8:48 Stage 4 11:03 Stage 5 And don't forget to subscribe for more videos like this 😊