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Will these new, efficient AI models send Nvidia's stock tumbling again?
- Nvidia faced a $600 billion loss due to concerns about less demand for its chips with the emergence of new, efficient AI models.
- New AI models like Google's Gemma 3 are showcasing impressive performance with minimal Nvidia chip usage.
- Cohere also launched a model called Command A, running on just two GPUs, demonstrating the trend toward more powerful and efficient AI.
- DeepSeek showed the capability of doing more with less, which led to Nvidia's significant stock market wipeout.
- Despite the threat posed by more efficient AI models, the Jevons Paradox suggests increased technology consumption as efficiency rises.
- Google plans to boost its AI-related capital expenditure, implying continued demand for Nvidia GPUs.
- While Google's Gemma models run on a single Nvidia GPU, their training involved Google's own TPUs, indicating a diverse chip usage strategy.
- Google's focus on developing both efficient and large-scale AI models suggests sustained demand for Nvidia GPUs in the near future.
- The market response to chip efficiency developments shows a limited impact on Nvidia's share price, which has actually risen since the news.
- AI development is moving in two directions: towards smaller, efficient models that run on fewer GPUs, and larger models that require significant GPU clusters for training.
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