AI Infra Engineering Roadmap: CUDA Kernels to vLLM in Three Stages

dhruv2038 · x · 2026-10-11

A three-stage learning path for AI infrastructure engineering:

Stage 1 Systems & GPU foundations: C++, Rust, CUDA memory hierarchy, PCIe vs NVLink, warp scheduling; practice by writing a custom matrix-multiply CUDA kernel and profiling it against cuBLAS.
Stage 2 Inference physics: prefill vs decode, arithmetic intensity, KV-cache math; practice profiling a HuggingFace model with Nsight Systems — LLM inference is almost always memory-bound, not compute-bound.
Stage 3 Serving engines & batching: vLLM, SGLang, PagedAttention, continuous batching, chunked prefill, with a 70B deployment exercise (thread truncated).

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