1 When PyTorch just isn’t enough
This chapter covers
- The CUDA programming model and parallel computing fundamentals
- Why deep learning operations are suited for GPU parallelization
- When to use CUDA versus PyTorch
- The optimization ladder from naive kernels to production performance
Large language models (LLMs) consume staggering compute budgets. The difference between a model that trains in days versus weeks, or infers in milliseconds versus seconds, often comes down to how effectively you use GPU hardware. CUDA is the programming layer where that optimization happens: the bridge between high-level frameworks like PyTorch and the physical silicon that executes your computations.
Raw CUDA programming remains the most direct path to squeezing every last ounce of performance from modern hardware.[1] This book arms you with the ability to recognize when you need a custom CUDA kernel instead of an existing library, and more importantly, how to create entirely new algorithms that push the boundaries of what’s computationally possible. By mastering CUDA at this level, you become one of the rare engineers capable of solving the performance bottlenecks that limit the next generation of AI systems.