The future of inference involves optimizing across three dimensions: quality, speed, and user concurrency, which relates to 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 mixture of experts model activates only a subset of parameters during training and inference, significantly lowering compute costs compared to traditional dense models.

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.

Implementing multi-query attention resulted in a fivefold reduction in GPU cache usage, which is crucial for efficiently generating responses.

As companies scale their AI applications, it's crucial to find the optimal balance across quality, cost, and speed to achieve sustainable growth.