AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A serving system that treats the diffusion block as a compilation unit that improves end-to-end execution time by up to 2.7 times over the strongest surviving baseline under the same 8-GPU placement and remains feasible at the largest batch sizes where multiple baselines run out of memory, while preserving task quality relative to the dense reference.
UniEP fuses the MoE communication and computation into MegaKernels, effectively transforming complex architectural tuning into a unified parameter search space for automated adaptability and incorporates a deterministic token ordering mechanism that guarantees numerical consistency with sequential execution, even under aggressive overlap schedules.
Prism abstracts the highly dynamic diffusion workload into a predictable, static execution flow transparent to the compiler, and achieves this via three techniques: spatial regularization, temporal stabilization, and specialized kernels that selectively bypass padding data.
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