VenusNOC

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   China's AI compute race shifts to systems engineering, Jiliu says

   InfoQ China published an interview with Chen Wei, an early member of Jiliu Technology, on how the question for domestic AI compute has shifted from simply getting accelerator cards to running them at scale. Once clusters grow from dozens to thousands of cards, the bottlenecks move to operator coverage, collective communication, scheduling and fault localization, and matching interfaces does not mean production-grade performance. Jiliu says its VenusCL communication component can add roughly 2%–6% more MFU even on thousand-card clusters customers had already tuned, and its VenusNOC Megatrace feature located a single faulty GPU in a roughly 3,800-card training cluster. The piece also covers heterogeneous co-working across chips from multiple vendors and generations, plus the trade-offs of ecosystem standardization, with Jiliu estimating that a 2%–6% gain could correspond to over 100 million yuan of existing compute value in one 10,000-card project.

   InfoQ 中文 AI&大模型 · 🔥 0

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