ChatGPT System Design | LLM Serving at Scale (100M Users, GPU Clusters, vLLM)

If you’ve ever wondered how systems like ChatGPT handle millions of users in real time — this video breaks it down from first principles to production-scale architecture 🚀

We deep dive into designing a ChatGPT-like system (LLM serving at scale) — covering everything from request flow to GPU optimization, caching strategies, and distributed architecture.

Whether you’re preparing for system design interviews, building AI products, or scaling LLM applications — this guide will give you a practical, real-world understanding.

🧠 What You’ll Learn
End-to-end architecture of LLM systems
High-level vs deep design breakdown
KV cache optimization and memory management
Handling high concurrency and low latency
Scaling strategies used in real-world AI systems
Trade-offs in distributed AI infrastructure

⚙️ Key Concepts Covered
LLM Serving Architecture
Token generation pipeline
Load balancing and request routing
GPU utilization strategies
Caching (KV Cache / PagedAttention)
Latency vs throughput trade-offs
Fault tolerance and scaling

🎯 Who Is This For?
Software Engineers preparing for system design interviews
Backend / Distributed Systems Engineers
AI Engineers building LLM applications
Tech enthusiasts exploring how ChatGPT works

🌍 GEO Relevance

This content is especially useful for engineers and developers in:

India 🇮🇳 (Bangalore, Hyderabad, Pune tech ecosystem)
USA 🇺🇸 (FAANG / Big Tech system design standards)
Europe 🇪🇺 (AI infrastructure and scaling startups)
🔥 Keywords (SEO Boost)

ChatGPT system design, LLM architecture, large language model serving, AI system design interview, distributed systems design, scalable AI systems, GPU inference optimization, KV cache, PagedAttention vLLM, backend architecture AI

#SystemDesign #LLM #ChatGPT #AIInfrastructure #DistributedSystems #BackendEngineering #Scalability

Видео ChatGPT System Design | LLM Serving at Scale (100M Users, GPU Clusters, vLLM) канала Arpit Vaish