Artificial IntelligenceAdvanced6 hours
Frontier AI Architectures: From Attention to Inference Scaling
Master modern LLM architectures, test-time compute scaling, and optimized inference engines.
Course Overview & Objectives
An end-to-end technical deep-dive into state-of-the-art transformer architectures, FlashAttention-3 kernels, Speculative Decoding, and test-time search algorithms. Built for senior AI engineers and systems architects.
Tariq Mansoor
Editor-in-Chief & Lead AI Architecture AnalystFocuses on deep learning architectures, inference scaling laws, and large-scale model deployment. Former systems engineer with 12+ years evaluating computational breakthroughs.
Enrollment & Access Status
Premium Technical Masterclass
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Curriculum Syllabus
2 Modules • 4 Lessons1
3 lessonsModule 1: Attention Mechanisms & Memory Hierarchies
Examining quadratic complexity, grouped-query attention, and hardware-aware FlashAttention algorithms.
Lesson 1.1: Multi-Head, Grouped-Query, and FlashAttention-3 Free Preview
Understanding why memory bandwidth dominates attention computation and how tiling saves SRAM transfers.
Lesson 1.2: KV Cache Optimization & PagedAttention
Deep dive into virtual memory management for LLM serving engines like vLLM and TensorRT-LLM.
Lesson 1.3: GPU Memory Bandwidth & Arithmetic Intensity
Roofline model analysis for transformer layers across prefill and decode stages.
2
1 lessonsModule 2: Inference Scaling & Test-Time Search
Understanding search rollouts, chain-of-thought verification, and speculative execution.