The future of computing will involve running incredibly large neural networks with high performance and low power consumption, requiring different solutions for training and inference workloads.

Deep learning's tolerance for reduced precision arithmetic and its reliance on a limited set of operations present opportunities for developing custom hardware that can significantly enhance computational efficiency.

The future of inference involves optimizing across three dimensions: quality, speed, and user concurrency, which relates to cost.

AI advancements are increasing GPU requirements, as the computational needs for inference are growing significantly.

The future of scaling inference will focus on heavy customization tailored to specific applications, transforming it into a multi-dimensional optimization problem.

The inference system design is not one-size-fits-all; it must utilize production data for maximum optimization, and combining post-training and inference can drive significant innovation and acceleration in AI applications, ultimately reducing inference costs by 10 to 100 times.

The future of computing will involve asynchronous workloads where AI systems perform extensive research before returning results, necessitating robust infrastructure to handle increased loads.

Both algorithmic improvements and computational advancements, such as better chips and larger data centers, are essential for progress in AI.

The reasoning models being developed by DeepSeek are designed to utilize more compute, emphasizing the growing importance of inference in complex AI tasks.