Unlocking Local LLMs: Deep Dive into Architecture & Python App Demo!

Welcome to the ultimate guide on running LLMs locally! In this video, we break down the inner workings of large language models—from their core architecture to the nitty‐gritty of inference at a lower level. You'll get an in‐depth explanation of how LLMs process data and manage memory, and see a live demo where I build a Python application powered by a locally running LLM.
In This Video:
•⁠ ⁠Introduction: Overview of local LLMs and why they matter
•⁠ ⁠LLM Architecture Explained: How modern LLMs are structured
•⁠ ⁠LLM Inference: Understanding the low-level processes in Unified Memory Architecture Computers(UMA)
•⁠ ⁠Python Demo: How to integrate it into any Python App.
•⁠ ⁠⁠Understanding Inference for non-unified memory architecture computers.

Creator:
   / @varunahlawat169  
Resources:
•⁠ ⁠⁠Neural Networks by 3B1B:    • Neural networks  
•⁠ ⁠⁠Deepseek: Limits Reasoning in Language Models(GRPO) https://arxiv.org/pdf/2402.03300
•⁠ ⁠⁠⁠Neural Scaling Laws for LLMs: https://arxiv.org/pdf/2001.08361

If you're a developer, AI enthusiast, or simply curious about advanced machine learning techniques, this video is for you. Don’t forget to like, comment, and subscribe for more cutting-edge AI tutorials!