Pro Frontier8.0 Hours•Level: Frontier•Updated: October 2026

High-Throughput LLM Serving & Custom Triton GPU Kernels

Break through the memory bandwidth wall. Understand NVIDIA GPU architecture (SMs, warps, tensor cores), write fused Triton kernels, and tune multi-GPU clusters running vLLM and TensorRT-LLM.

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Curriculum Outcomes & Engineering Deliverables

Profile GPU bottlenecks using NVIDIA Nsight Compute and Roofline models

Write custom Triton kernels with on-chip SRAM tile caching

Deconstruct FlashAttention-2 & FlashAttention-3 online softmax algorithms

Optimize multi-GPU Tensor Parallelism and Continuous Batching clusters

Included Engineering Deliverables

Custom fused RoPE + GeLU Triton kernel implementation

FlashAttention-3 online softmax tile-based forward kernel

NCU (Nsight Compute) roofline profiling and memory coalescing harness

Megatron-LM style Tensor Parallel column/row linear layers

Curriculum Modules

3 Modules • 5 Lessons

Module 012 Lessons

GPU Hardware Architecture for LLMs

Understanding the silicon: Streaming Multiprocessors, Warps & HBM.

Streaming Multiprocessors, Memory Hierarchies & Coalescing

Interactive • 38 min

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The Roofline Model: Compute-Bound vs. Memory-Bound Operators

Deep Dive • 40 min

Pro

Module 022 Lessons

Custom Triton GPU Kernels

Write hardware-accelerated kernels in Python without raw CUDA boilerplate.

Writing Your First Fused RoPE & GeLU Kernel in OpenAI Triton

Code Lab • 45 min

Pro

FlashAttention-2 vs FlashAttention-3: Online Softmax Algorithm

Architecture • 48 min

Pro

Module 031 Lessons

Cluster Scale Serving & Parallelism

Scaling inference across multi-node GPU clusters.

Tensor Parallelism, Pipeline Parallelism & Continuous Batching

Deep Dive • 44 min

Pro

Hardware & GPU Compute

Baseline GPUNVIDIA Ada Lovelace (RTX 4090), Ampere (A100), or Hopper (H100)

VRAM Requirement24GB - 80GB VRAM

Recommended Configuration1x NVIDIA H100 80GB SXM5 (via Lambda / RunPod template)

Prerequisites & Tech Stack

Stack Components

OpenAI Triton 3.0CUDA 12.4Nsight Compute (NCU)PyTorch C++TensorRT-LLM

Knowledge Prerequisites

C/C++ or PyTorch internal familiarity
Basic GPU computing concept
LLM Foundations