In this 20-minute AI Inference Crash Course, I’ll help you master AI engineering, covering caching, GPUs, diffusion models, and the fundamentals you need to master AI inference in production.
I’ve covered the major techniques used to optimize inference, including GQA, prefix caching, PagedAttention, continuous batching, and speculative decoding. I’ve also explained how distributed inference, parallelism strategies, model routing, quantization, and cost optimization fit into the serving stack. I’ll also show how diffusion models and local inference fit into the bigger picture.
If you work in cloud, backend, AI infrastructure, or platform engineering, or you’re preparing for AI system design interviews, this video will help you build a stronger understanding of how modern AI systems are served and optimized in production.
CHAPTERS:
00:00 Introduction
01:17 Chapter 1: What Is AI Inference?
02:18 Prefill
03:47 Decode
04:55 Inference Engine
06:29 Inference Metrics
08:03 Chapter 2: Cache Optimization
08:29 Architectural
09:19 Prefix Caching
10:03 Paged Attention
11:29 Batching Techniques
12:44 Speculative Decoding
13:32 Quantization
14:24 Chapter 3: Serving Infrastructure
14:41 Parallelism
16:34 AI Infrastructure Components
17:50 Model Routing & Cascading
19:28 Chapter 4: Diffusion Model
20:13 Chapter 5: Running Models Locally
21:03 Key Takeaways
22:52 Outro
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🙋♀️ ABOUT ME
I’m Vishakha ☁️; A Cloud Architect and DevOps enthusiast who loves turning complex cloud concepts into simple, practical insights you can actually use. You’ll find real-world projects 💻 quick explainers ☕ and honest career advice 💬; From Kubernetes and Terraform to landing roles and building your tech brand, I keep things approachable, actionable, and a little fun so learning Cloud and DevOps actually feels exciting.
If this sounds like your vibe, hit subscribe, and let’s keep growing together 🚀
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vishakha sadhwani,cloud computing,ai inference,llm full course,llm inference,kv cache,vllm,tensorrt,sglang,speculative decoding,quantization,ai infrastructure,ai system design,system design,system design interview,ai engineer,ai engineering,mlops,devops engineer,cloud engineer,software engineer,platform engineer,artificial intelligence,ai,machine learning,ai engineer crash course,ai engineer interview questions,hugging face,run llm locally,coding
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