@TheAhmadOsman: How to go about learning all of this? 1st: Start with the serving engine view - vLLM: PagedAttention, continuous batchi…

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Summary

A detailed guide on learning AI inference engine internals, covering serving engines like vLLM and SGLang, low-level GPU kernel programming with Triton and CUTLASS, and a sequence of mini-projects to build hands-on expertise.

How to go about learning all of this? 1st: Start with the serving engine view - vLLM: PagedAttention, continuous batching, prefix caching, CUDA graphs - SGLang: RadixAttention/prefix reuse, speculative decoding, MoE, structured/agent workloads - TensorRT-LLM: NVIDIA peak stack, FP8/FP4, Wide-EP, disaggregated serving - FlashInfer: reusable kernel/operator library for attention/GEMM/MoE/sampling 2nd: Go down the stack - Triton tutorials → custom fused kernels - CUTLASS/CuTe → Tensor Core GEMM and Blackwell/Hopper details - FlashAttention papers → attention algorithm/kernel co-design - PagedAttention paper → KV-cache memory management - MoE docs → routing + grouped GEMM + all-to-all - Nsight profiling → stop guessing 3rd: Do this mini-project sequence 1. Implement RMSNorm in Triton; compare to PyTorch 2. Implement fused SiLU × gate 3. Implement simple FP16 matmul; compare to cuBLAS/rocBLAS 4. Implement paged KV lookup for decode attention 5. Add FP8 KV cache with per-block scales 6. Implement toy top-k sampling on GPU 7. Implement tiny MoE dispatch + grouped GEMM 8. Integrate one custom op into vLLM or SGLang and profile end-to-end
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Cached at: 06/08/26, 11:19 AM

How to go about learning all of this?

1st: Start with the serving engine view

  • vLLM: PagedAttention, continuous batching, prefix caching, CUDA graphs

  • SGLang: RadixAttention/prefix reuse, speculative decoding, MoE, structured/agent workloads

  • TensorRT-LLM: NVIDIA peak stack, FP8/FP4, Wide-EP, disaggregated serving

  • FlashInfer: reusable kernel/operator library for attention/GEMM/MoE/sampling

2nd: Go down the stack

  • Triton tutorials → custom fused kernels

  • CUTLASS/CuTe → Tensor Core GEMM and Blackwell/Hopper details

  • FlashAttention papers → attention algorithm/kernel co-design

  • PagedAttention paper → KV-cache memory management

  • MoE docs → routing + grouped GEMM + all-to-all

  • Nsight profiling → stop guessing

3rd: Do this mini-project sequence

  1. Implement RMSNorm in Triton; compare to PyTorch

  2. Implement fused SiLU × gate

  3. Implement simple FP16 matmul; compare to cuBLAS/rocBLAS

  4. Implement paged KV lookup for decode attention

  5. Add FP8 KV cache with per-block scales

  6. Implement toy top-k sampling on GPU

  7. Implement tiny MoE dispatch + grouped GEMM

  8. Integrate one custom op into vLLM or SGLang and profile end-to-end

I’ll try to put together something practical this week

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