{"id":273513,"date":"2024-07-03T14:35:13","date_gmt":"2024-07-03T14:35:13","guid":{"rendered":"https:\/\/www.techopedia.com\/?p=273513"},"modified":"2024-07-03T14:35:13","modified_gmt":"2024-07-03T14:35:13","slug":"is-america-falling-behind-china-in-the-llm-race","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/is-america-falling-behind-china-in-the-llm-race","title":{"rendered":"Is America Falling Behind China in the LLM Race? Yes\u2026 and No"},"content":{"rendered":"

The large language model<\/a> (LLMs) race is heating up, and there’s good reason to suggest that the gap between the U.S. and other countries like China is closing.<\/p>\n

Earlier this week, HuggingFace, a leading platform for AI research and benchmarking, released the Open LM Leaderboard v2<\/strong><\/a> with new benchmarks, saying it was time to revisit how learning models were rated, with the gap closing between top LLMs.<\/p>\n

The new leaderboard features Qwen-2-72B<\/strong> Instruct in the number one spot, a model developed in China by Alibaba Cloud. Another Chinese model featured in fourth place; Yi 1.5<\/strong>.<\/p>\n

This highlights that China is gaining a deeper foothold in the world of open-source<\/a> AI development and closing in on key performance benchmarks across the board.<\/p>\n

While the U.S. still has a rich ecosystem of providers, including OpenAI<\/a>, Anthropic<\/a>, Google Claude<\/a>, Microsoft<\/a>, Meta<\/a>, Amazon<\/a>, and Nvidia<\/a>, the gap in performance is still gradually closing. Here’s why.<\/p>\n

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Key Takeaways<\/span><\/h2>\n