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Production-grade LLM inference requires benchmarking across three pillars: end-to-end model performance, micro-benchmarking of isolated components, and a structured improvement process. The prefill phase is compute-bound, while the decode phase is memory-bound. Hardware variability across NVIDIA and AMD GPUs makes optimal performance dependent on software maximizing FLOPs and bandwidth efficiency.
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