NVIDIA

The key idea for enabling the out-of-core approach for scaling UMAP is constructing the all-neighbors kNN graph without requiring the entire dataset to fit in GPU memory at once, as introduced in the previous post. The approach accomplishes this by partitioning the dataset into balanced clusters and overlapping the vectors across nearby clusters to preserve nearest-neighbor relationships across the cluster boundaries. Local kNN graphs are computed independently for each cluster, and these local graphs are merged into a single global all-neighbors graph. This makes it possible to run UMAP at sc

NVIDIA's RTX Spark is an ARM-powered SoC that's expected to compete directly with the likes of Intel, AMD, and Qualcomm for space in Windows 11 PCs. I covered rumors of the "N1X" chip for months in the lead-up to Computex 2026, and indeed it seems just as powerful and efficient as pre-announcement estimates. Spark takes efficient ARM-based CPU cores similar to those found in Qualcomm Snapdragon chips and pairs them with an NVIDIA Blackwell RTX GPU, all while drawing from a unified memory setup that can hit 128GB. This combination allows for massive performance that can shred standard and AI-re

The report says NVIDIA is targeting TSMC's A16 1.6 nm process for Feynman. This is a jump from Rubin and Rubin Ultra, which are based on TSMC's N3 (3 nm) process. Answered

Skipping TSMC's N2/2nm process is notable because NVIDIA is jumping straight to TSMC's more advanced A16 1.6nm node for Feynman, representing a major node shrink from the 3nm Rubin architectures. The move is also significant because Feynman will be NVIDIA's first technology to use 3D chiplets via TSMC's SoIC technology, backside power delivery, and next-generation packaging, along with custom next-generation HBM and co-packaged optics to boost performance and bandwidth. Answered