LLMSpace is the first framework to jointly model operational and embodied carbon for LLM inference on LEO satellites, incorporating radiation-hardened hardware, peripheral systems, and workload patterns such as prefill-decode behavior.
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A single global merge at the final step of decentralized SGD matches the convergence rate of parallel SGD while improving test accuracy under high data heterogeneity.
Introduces Switching Efficiency (η) decomposed into data, routing efficiency, and port utilization factors to analyze and improve communication bottlenecks in AI data center networks for LLM training.
U.S. operators control 48% of non-U.S. data center projects by investment value, limiting digital sovereignty for host nations and offering the U.S. an additional governance tool for deployed AI infrastructure.
citing papers explorer
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LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites
LLMSpace is the first framework to jointly model operational and embodied carbon for LLM inference on LEO satellites, incorporating radiation-hardened hardware, peripheral systems, and workload patterns such as prefill-decode behavior.
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On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning
A single global merge at the final step of decentralized SGD matches the convergence rate of parallel SGD while improving test accuracy under high data heterogeneity.
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Switching Efficiency: A Novel Framework for Dissecting AI Data Center Network Efficiency
Introduces Switching Efficiency (η) decomposed into data, routing efficiency, and port utilization factors to analyze and improve communication bottlenecks in AI data center networks for LLM training.
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How Sovereign Is Sovereign Compute? A Review of 775 Non-U.S. Data Centers
U.S. operators control 48% of non-U.S. data center projects by investment value, limiting digital sovereignty for host nations and offering the U.S. an additional governance tool for deployed AI infrastructure.