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3 Ways to Boost MoE Inference on NVIDIA DGX Spark

Abstract green computing cube with layered panels, performance icons, and streams of data on a black backgroundGenerating tokens faster starts with identifying what limits inference performance. In the batch-size-1 evaluation of Ling-3.0-flash from Inclusion AI on NVIDIA...Abstract green computing cube with layered panels, performance icons, and streams of data on a black background

Generating tokens faster starts with identifying what limits inference performance. In the batch-size-1 evaluation of Ling-3.0-flash from Inclusion AI on NVIDIA DGX Spark, profiling showed that decoding was memory-bound: reading model weights drove execution time. Two operations stood outβ€”the mixture-of-experts (MoE) computation and the output-layer projection that produces scores across the model’…

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Lagrangian–Eulerian learning of flow field and trajectories with TrajectoryFlowNet

Nature Machine Intelligence, Published online: 20 October 2026; doi:10.1038/s42256-026-01316-7

Wan et al. introduce a physics-informed learning model that simultaneously predicts long-range particle paths and flow fields based on very limited trajectory measurements, validated in both simulations and blood-flow experiments.
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