Inference Engineering Learning Path: From Basics to TensorRT-LLM
HowDevelop · x · 2026-08-31
This post compiles a learning resource list for Inference Engineering, covering the full spectrum from model principles to low-level optimization. Key components include:
- Foundations: ML basics, math, and the 'Attention is all you need' paper.
- Core Resources: 'Inference Engineering' by Philip Kiely.
- Low-level Tech: GPU internals, CUDA programming, and profiling/benchmarking with Nvidia TensorRT-LLM.
Multiple relevant links are provided, serving as a roadmap for deep diving into inference optimization.
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