Learn about the 5 common stages of the inference workflow, from initial setup to edge deployment, and how AI accelerator needs shift throughout the process.

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A comprehensive framework for selecting AI accelerators across five inference workflow stages: initial setup, performance tuning, production deployment, large model serving, and edge deployment. Each stage has distinct hardware requirements—from L40S and A10 for early testing and optimization, to H100/H200 for large language models requiring multi-GPU setups, to low-power L4 and T4 for edge environments. The guide covers key considerations like memory capacity, bandwidth, power consumption, and interconnect capabilities, with practical examples showing how accelerator choice impacts latency, throughput, and cost at each deployment phase.

AI accelerator selection for inference: A stage-based framework