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A curated list of resources for mastering GPU engineering for AI systems, covering CUDA, ROCm, optimization tools, multi-GPU orchestration, and distributed training.
A curated GitHub list of resources for learning GPU engineering, covering architecture, kernel programming, optimization, distributed systems, and AI acceleration with books, frameworks, profilers, and interview prep.
This article maps the optimal AI-augmented path to becoming a GPU/CUDA engineer, highlighting compensation ranges and the growing demand for inference optimization specialists. It provides a realistic timeline and emphasizes the use of AI tools to accelerate learning.