Achieving this reduction in inference costs requires solving a complex three-dimensional optimization problem involving quality, speed, and cost.

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 current high inference costs resemble an iceberg, where reducing these costs can enable applications to scale into sustainable businesses.

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.

The future of inference hardware may include analog computing due to its power efficiency, alongside advancements in specialized digital hardware.

DeepSeek's recent models, including V3, highlight the importance of balancing training and inference compute resources to optimize performance and efficiency.

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

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