The scaling of compute resources will vary significantly depending on the problem, with some requiring up to 10,000 times more compute than others, which allows for more capable models.

DeepSeek's training run with 2,000 GPUs demonstrates that while they can still train models effectively, strong export controls could limit their ability to scale further.

We faced unique challenges in scaling our platform, particularly with GPU local attention caching, which led to unexpected negative results during testing.

Scaling and training very large neural networks has proven effective, as demonstrated by our training of a neural network 60 times larger than previous models using 16,000 CPU cores.

Performance issues in graph-based systems often arise when scaling to enterprise levels, affecting both accuracy and system reliability.

The nature of data center tasks is shifting from traditional database access to inference and training, which require thousands of GPUs to be connected in high-speed networks for efficiency.

While a 10 million GPU synchronous pre-training run may not look like current methods, there will likely be some form of training run at that scale in the future, and I would call the learning process semi-synchronous, as it involves many GPUs working together on an AI system, but not all parts will necessarily communicate with each other.

The largest AI models, like GPT-4, require massive GPU clusters, with power consumption reaching unprecedented levels, highlighting the increasing scale of AI infrastructure.

The scaling laws in AI are still valid, but as we scale, challenges such as data management and system complexity become more pronounced, necessitating innovative solutions.