AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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End-to-end case studies show that sparse workloads favor criticality-aware heterogeneous pods, whereas LLM inference favors scale-up islands; the resulting designs deliver 6.20 and 2.12 geomean speedups, respectively, while reducing cost and power relative to the baselines.
This work proposes Think-at-Hard (TaH), a looped transformer optimized for selective iteration that employs a lightweight neural decider to trigger latent iteration, only at tokens likely to be incorrect after the standard forward pass.
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