AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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ThunderGNN is a hardware-aware acceleration system designed to reconcile graph irregularity with Tensor Core rigidity and significantly outperforms state-of-the-art systems, achieving geometric mean speedups of 1.89× over DGL and 2.59× over PyG.
This work identifies a structural connection between speculative decoding (SD) and length prediction: latent representations produced by the draft decoder in advanced frameworks (e.g., EAGLE-3) encode signals that are predictive of generation length.
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