The authors introduce a compute-dependent versus compute-independent framework and report nanoGPT experiments showing compute-independent algorithms such as LayerNorm and RoPE give compute-equivalent gains up to 1.9x, while compute-dependent ones become neutral only as model size grows.
Whack-a-Chip: The Futility of Hardware-Centric Export Controls
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abstract
U.S. export controls on semiconductors are widely known to be permeable, with the People's Republic of China (PRC) steadily creating state-of-the-art artificial intelligence (AI) models with exfiltrated chips. This paper presents the first concrete, public evidence of how leading PRC AI labs evade and circumvent U.S. export controls. We examine how Chinese companies, notably Tencent, are not only using chips that are restricted under U.S. export controls but are also finding ways to circumvent these regulations by using software and modeling techniques that maximize less capable hardware. Specifically, we argue that Tencent's ability to power its Hunyuan-Large model with non-export controlled NVIDIA H20s exemplifies broader gains in efficiency in machine learning that have eroded the moat that the United States initially built via its existing export controls. Finally, we examine the implications of this finding for the future of the United States' export control strategy.
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cs.LG 1years
2025 1verdicts
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Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progress
The authors introduce a compute-dependent versus compute-independent framework and report nanoGPT experiments showing compute-independent algorithms such as LayerNorm and RoPE give compute-equivalent gains up to 1.9x, while compute-dependent ones become neutral only as model size grows.