Make AI Run 10x Faster
Write the GPU Code Frontier Labs Fight to Hire — No CUDA Required
Everyone can call PyTorch. Almost nobody can write the kernels underneath it. Build FlashAttention from scratch—in Python—and join the rarest tier of AI engineers.
The deepest moat in AI isn't the model—it's the kernel layer that makes models fast. Until recently that meant years of CUDA and C++. Triton changed everything: an open-source, Python-based GPU language built into PyTorch 2 and backed by NVIDIA, Intel, and AMD. In this masterclass, you'll go from zero GPU programming experience to writing production-grade kernels: memory coalescing, tiling, shared memory, reductions, and fusion—then build the kernels that power modern AI, including FlashAttention, native sparse attention, and sparse matrix multiplication. You'll profile real workloads, find real bottlenecks, and integrate your kernels end-to-end into PyTorch. This is how you make training and inference 10x faster—and how you become the engineer nobody can replace.
Your Competitive Moat
AI Hyper-Personalizes Your Experience
This isn't a one-size-fits-all course. It's assessed to your gaps, adapted to you, and finished with a custom deliverable you build and own.
Pre-Masterclass Assessment
You begin with an AI-driven assessment that maps what you already know against everything this masterclass covers. We pinpoint your knowledge gaps up front—so your time goes only where it moves the needle.
An AI-Personalized Path
Your results reshape the masterclass around you. The AI aligns the material, examples, and pace to close your specific gaps—so a fixed curriculum becomes a path built for exactly one person: you.
A Custom Deliverable You Own
You don't leave with a certificate—you leave with a real, working artifact built for your goals. In "Make AI Run 10x Faster," that means a deliverable you can ship, show, and build on. Something you made, not just something you watched.
Proven Transformation Results
Real outcomes from students who completed The LLM Sovereignty Stack™ and built their competitive moats
📈 Career Transformation
💰 Business Impact
What You'll Actually Build
Choose Your Path to Mastery
All modalities include the complete LLM Sovereignty Stack™. Choose based on your learning style and goals.
Self-Paced Mastery
- All 8 modules available immediately
- Lifetime access to content and updates
- Community support and code reviews
- Monthly live office hours
- Cloud GPU setup guides included
8-Week Live Cohort
- Weekly live workshops with Dr. Lee
- Kernel code reviews and profiling sessions
- Direct instructor access
- Graduation certificate
- Alumni network access
Founder's Edition
- One-on-one mentorship with Dr. Lee
- Kernel-level optimization of YOUR workload
- Inference cost reduction roadmap
- 90-day satisfaction guarantee
5-Day Immersive Bootcamp
Executive intensive format. Build FlashAttention in one week. Live profiling labs on real hardware.
Course Curriculum
8 transformative steps · 40 hours of hands-on content
Module 1: How GPUs Actually Work
5 lessons · Shu-Ha-Ri cycle
- The GPU Mental Model: Thousands of Threads, One Goal
- Memory Hierarchies: Global, Shared, Registers—and Why They Rule Performance
- Why Frameworks Hit a Wall: The Case for Custom Kernels
- Triton vs CUDA: Block-Level Programming Explained
- Setting Up Your Kernel Development Environment
Module 2: Your First Triton Kernels
5 lessons · Shu-Ha-Ri cycle
- Anatomy of a Triton Kernel: Programs, Blocks, and Pointers
- Vector Addition to Softmax: The Foundational Patterns
- Masking and Boundary Conditions Done Right
- Benchmarking Honestly: Measuring Against PyTorch Baselines
- Hands-On: Ship Your First Kernel That Beats the Baseline
Module 3: Memory Coalescing & Tiling
5 lessons · Shu-Ha-Ri cycle
- Why Memory Access Patterns Make or Break Performance
- Coalescing: Aligning Threads with Memory Transactions
- Tiling: Decomposing Big Problems into Fast Blocks
- Matrix Multiplication: The Canonical Tiled Kernel
- Hands-On: Build a Tiled MatMul That Rivals cuBLAS
Module 4: Shared Memory & Reductions
5 lessons · Shu-Ha-Ri cycle
- Shared Memory: The Programmable Cache
- Reduction Patterns: Sums, Maxes, and Normalizations at Speed
- Numerical Stability in Parallel Reductions
- LayerNorm and RMSNorm Kernels from Scratch
- Hands-On: Build the Normalization Kernels Inside Every LLM
Module 5: Kernel Fusion
5 lessons · Shu-Ha-Ri cycle
- Why Fusion Wins: Eliminating Round-Trips to Global Memory
- Fusing Elementwise Chains: Bias, Activation, Dropout in One Pass
- On-Chip Fused Operations for Transformer Blocks
- When NOT to Fuse: Register Pressure and Occupancy Tradeoffs
- Hands-On: Fuse a Transformer Sub-Block and Measure the Win
Module 6: Build FlashAttention from Scratch
5 lessons · Shu-Ha-Ri cycle
- Why Naive Attention Is Memory-Bound: The O(N²) Problem
- The FlashAttention Insight: Tiling + Online Softmax
- Implementing the Forward Pass Block by Block
- Handling Causal Masking Efficiently
- Hands-On: Your FlashAttention vs PyTorch SDPA—Head to Head
Module 7: Sparse Attention & Sparse Compute
5 lessons · Shu-Ha-Ri cycle
- Native Sparse Attention: The Frontier of Efficient Transformers
- Sparse Matrix Multiplication Kernels
- Block-Sparse Patterns: Trading Density for Speed
- Where Sparsity Pays Off in Real Models
- Hands-On: Implement Native Sparse Attention
Module 8: Profiling & Production Integration
5 lessons · Shu-Ha-Ri cycle
- Profiling Real Workloads: Finding the Actual Bottleneck
- Autotuning: Letting Triton Search the Configuration Space
- Integrating Custom Kernels into PyTorch Models End to End
- Testing and Wrapping Kernels for Production
- Capstone: Accelerate a Real Model with Your Own Kernel Suite
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
No. This masterclass assumes zero GPU programming background. Triton's block-level model in Python removes the thread-wrangling that makes CUDA brutal for beginners. If you can write NumPy-style Python, you can learn to write kernels.
A consumer NVIDIA card (or a cloud GPU) is enough for every exercise. We provide cloud GPU setup guides, and the profiling techniques you learn transfer directly to datacenter hardware.
Because kernel engineers are the scarcest talent in AI. Frameworks are built ON kernels—Triton is literally inside PyTorch 2. When your model is memory-bound and the framework has nothing left to give, the engineer who can write a fused kernel is the one who ships. That skill commands frontier-lab compensation.
It's the layer beneath everything else in the catalog. Build Your Own LLM teaches the architecture; Build Your Own Frontier AI teaches efficiency at the model level; this masterclass teaches the silicon level—the kernels that make attention, normalization, and MoE actually fast.
Stop Renting AI. Start Owning It.
Join 500+ engineers and founders who've gone from API consumers to model builders—building their competitive moats one step at a time.
Command $250K-$400K salaries or save $100K-$500K in annual API costs. Own your model weights. Build defensible technology moats. Become irreplaceable.
Self-paced · Lifetime access · 30-day guarantee
Start Your TransformationThis is not just education. This is technological sovereignty.