VizuaraAI

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3 Chapters · 73 Topics · 200+ Visual Diagrams · 400+ Pages Most AI resources give you either theory without intuition, or code without understanding. This resource gives you both: every concept explained visually, from a single neuron to distributed inference pipelines.

A Complete Visual Guide to Mastering Modern AI & LLMs
 

Understand and build end to end LLM inference systems which run on laptops, mobile phones and edge devices like Raspberry Pi.

CTO at Vizuara AI, MIT PhD

Massachusetts Institute of Technology

IIT Madras

Vizuara

Dr. Raj Dandekar

MIT PhD | CTO at Vizuara AI Labs | Researcher

AI PM at Apple, Mckinsey, MIT PhD

Yash Dixit

Apple AI/ML | MIT | Ex-McKinsey | Research mentor for publication-track students

LLMs are everywhere: but deploying them in production is a completely different challenge from training them. Most engineers struggle with latency, GPU memory limits, and skyrocketing serving costs. The gap between "it works in a notebook" and "it serves 10,000 users" is inference engineering.This is the first course dedicated entirely to production-level LLM inference. You won't just learn the theory: you will deploy models on real hardware (Raspberry Pi, Jetson Orin Nano, Android phones), optimize inference pipelines using vLLM, TensorRT-LLM, and FlashAttention, and build two capstone projects: a speed-optimized inference server and a self-improving WhatsApp AI assistant.You will also learn directly from 9 guest engineers at Anthropic, NVIDIA, Apple, Microsoft, AWS, and more: the people actually building inference infrastructure at scale.By the end, you'll have the skills to take any open-source LLM from raw weights to a fully deployed, optimized production pipeline. Whether you're an ML engineer, backend engineer moving into AI, or a researcher who wants to ship — this course bridges the gap.200+ engineers have already enrolled.

45 hours of live LLM inference lectures, 4 hardware labs, 9 guest  lectures

Understand the Complete LLM Inference Stack

Master GPU Architecture & Performance Analysis

Quantize Models for Real-World Deployment

Build with Production Inference Frameworks

Accelerate Generation with Speculative Decoding & Advanced Serving

Deploy on Real Hardware: Not Just Cloud GPUs

ML &amp; backend engineers who can build models but struggle with serving them at low latency, optimizing GPUs, and deploying to production.

<blockquote>Researchers &amp; data scientists who understand transformers but have never optimized inference or deployed models on real hardware.</blockquote>

Tech leads &amp; architects evaluating inference infrastructure: vLLM vs SGLang, quantization tradeoffs, and GPU memory planning.

Learn directly from VizuaraAI

VizuaraAI

Courses

LLM Inference Engineering: Theory, Practicals and Research

Lightning Lessons

Build a Personal Knowledge LLM Wiki (Andrej Karpathy style)

LLM Inference: The Skill Every AI Engineer Is Missing

Resources

A Complete Visual Guide to Mastering Modern AI & LLMs

A Complete Visual Guide to Mastering Modern AI & LLMs