Huang also introduced the concept of three active AI scaling laws: pre-training, post-training, and test-time compute, which he believes will further accelerate AI development. Despite skepticism about the slowing progress of AI, Huang remains optimistic, suggesting that Nvidia's advancements will continue to reduce costs and enhance performance. He emphasized that Nvidia's AI chips are now 1,000 times better than those from a decade ago, surpassing the traditional expectations of Moore's Law.
Key takeaways:
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- Nvidia CEO Jensen Huang claims that the performance of Nvidia's AI chips is advancing faster than Moore's Law, with their latest datacenter superchip being more than 30x faster for AI inference workloads than the previous generation.
- Huang suggests that AI progress is not slowing and introduces three active AI scaling laws: pre-training, post-training, and test-time compute, which contribute to AI model advancements.
- There are concerns about the cost of running AI models with test-time compute, but Huang believes that more performant chips will eventually lower these costs, making AI reasoning models more affordable.
- Huang asserts that Nvidia's AI chips today are 1,000x better than those from 10 years ago, indicating a much faster pace of advancement than Moore's Law, and he expects this trend to continue.