Inference Learning Hub

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New: Interactive quizzes and hands-on exercises

Master Disaggregated Inference in LLM Serving

Learn cutting-edge techniques for optimizing large language model performance, reducing latency, and improving resource utilization in production environments.

Trusted by engineers worldwide

6 Comprehensive Modules

From LLM serving challenges to advanced optimization techniques and production deployment strategies.

Learn at Your Own Pace

Lifetime access to all course materials. Complete sections in order and track your progress along the way.

Certificate of Completion

Earn a professional certificate upon completing all sections to showcase your expertise.

What You'll Learn

Understanding LLM serving challenges and bottlenecks
The two-phase inference process: prefill and decode
Architecting disaggregated systems for optimal performance
KV cache management and efficient transfer techniques
Performance optimization strategies and batching
Real-world implementation and deployment patterns
Advanced topics and future research directions

One-time payment

$249

Students save $100 with .edu email

Who This Course Is For

This course is designed for technical professionals who want to master production-grade LLM serving optimization

AI/ML Engineers & Researchers

Building or optimizing LLM inference pipelines

ML Infrastructure / MLOps Engineers

Managing production serving at scale

Data Scientists

Transitioning to systems-level optimization for large models

Startup Founders & Tech Leads

Deploying cost-efficient LLM services (chatbots, agents, APIs)

Students & Professionals

Preparing for roles in frontier AI companies (xAI, OpenAI, Anthropic)

Production Engineers

Scaling inference infrastructure for enterprise applications

Note: This course assumes basic familiarity with LLMs, Python, and inference concepts. Not for absolute beginners.

How This Course Will Accelerate Your Career in AI

Invest in skills that deliver tangible ROI and position you at the forefront of LLM infrastructure

Stand Out in High-Demand Roles

Expertise in disaggregated inference (prefill-decode separation, KV-cache optimization) is rare and increasingly required at top AI labs and inference providers (Fireworks, Groq, Together AI).

Reduce Serving Costs Dramatically

Learn techniques that cut GPU bills 2-5x in production – directly impacting company bottom line and your value as an engineer.

Build Production-Grade Systems

Master real-world patterns used by leading teams (vLLM, SGLang, NVIDIA Dynamo) to handle bursty workloads and scale to thousands of GPUs.

Future-Proof Your Skills

Stay ahead of trends like heterogeneous hardware, Attention-FFN disaggregation, and open-source breakthroughs (DeepSeek, LMCache) – positioning you for 2026-2028 advancements.

Earn a Verifiable Credential

Share your Certificate of Completion on LinkedIn/X/resume to signal deep systems knowledge in the fast-moving LLM space.

Lifetime Access + Updates

Revisit materials as the field evolves (new frameworks, hardware like Rubin/Blackwell impacts). One payment, continuous learning.

What Early Learners Are Saying

Join engineers and researchers already mastering disaggregated inference

"The course explains complex LLM serving concepts in a really clear and structured way. I especially liked how it combines system design ideas with practical exercises. It's a solid resource for engineers who want to understand modern large-scale inference systems."

Chris Harry Patrick

AI/ML Engineer

"This course finally connected the dots between research papers and production serving. The KV-cache transfer deep-dive alone saved our team weeks."

ML Engineer

AI Startup

"Perfect mix of theory and practical deployment patterns. Highly recommend for anyone scaling LLMs in production environments."

MLOps Lead

Tech Company

"Clear explanations of why colocation fails and how disaggregation fixes it. Eye-opening content for systems engineers."

AI Researcher

Research Lab

Why Algorithmic Efficiency Matters More Than Ever

"Most people in the AI community don't yet understand this: the intelligence density potential is vastly greater than what we're currently experiencing. We could extract 100x more intelligence per gigabyte, per watt, per transistor — just from algorithmic improvements alone."

Elon Musk on intelligence density and algorithmic optimization

Frequently Asked Questions

Everything you need to know about the course

You should have basic familiarity with LLMs, Python programming, and general inference concepts. This course is not designed for absolute beginners - it assumes you understand what transformer models are and have some experience with ML systems.

The course is self-paced and most learners complete it within 2-4 weeks, spending 3-5 hours per week. You have lifetime access, so you can take as much time as you need and revisit materials whenever you want.

Yes! After completing all 6 modules and passing the quizzes, you'll receive a digital Certificate of Completion that you can share on LinkedIn, your resume, or portfolio to showcase your expertise in disaggregated inference.

It's a one-time payment of $249. You get lifetime access to all course materials, including any future updates and additions. No recurring fees or hidden costs.

Yes! The field of LLM serving is rapidly evolving. We regularly update the course with new frameworks, hardware considerations (like NVIDIA Blackwell/Rubin), and emerging techniques to ensure you stay current with the latest developments.

Absolutely! The course platform is fully responsive and works seamlessly on desktop, tablet, and mobile devices. You can learn anywhere, anytime.

Yes! If you're looking to train multiple team members, please contact us through the contact page for volume discounts and enterprise licensing options.

While there are research papers and blog posts available, this course provides a structured, comprehensive curriculum that connects theory to practice. You get hands-on exercises, quizzes to test your knowledge, real-world implementation patterns, and a clear learning path - all in one place.

Still have questions?

Ready to Master LLM Serving?

Join engineers and researchers worldwide building the next generation of AI infrastructure

Lifetime access • Students pay only $149