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torch.compile and CUDA Graphs

torch.compile optimizes captured computation, while CUDA graphs reduce repeated launch overhead. Both need validation against the model’s shapes, control flow and memory behavior.

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Kernel Fusion CUDA Programming Model Triton Programming Model Bandwidth-Bound Decode Throughput Serving Engines: vLLM, SGLang and TensorRT-LLM Profiling with Nsight

LESSONS THAT TEACH THIS

Kernels and the MachineLaunch overhead is real, and small kernels are where it eats you15 min→

PRACTICE THIS IN REAL QUESTIONS

CUDA, Triton & Kernel EngineeringWhat does torch.compile actually do to your model, and when does it fail to help?→CUDA, Triton & Kernel EngineeringWhy do serving engines capture decode steps into CUDA graphs, and what does capture require of the rest of the code?→Open-Weights Models & Serving EnginesThe engine logs say CUDA graphs are disabled for your deployment. Does it matter?→CUDA, Triton & Kernel EngineeringWhy fuse kernels, how much does it save, and what can fusion not fix?→Open-Weights Models & Serving EnginesYour model decodes at a tenth of its bandwidth bound at batch one. Explain the gap.→Distributed Training & ParallelismFSDP or DeepSpeed ZeRO-3: which would you pick for a new training codebase today, and why?→