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

Many app developers currently rely on off-the-shelf models, but infusing product knowledge into these models through prompt engineering is a new and necessary skill.

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

The future will likely see more researchers embedded in every product team, as fine-tuning models for specific use cases becomes a core workflow for building products.

Decentralized inference providers gained traction as they offered models that met market demand, resulting in a rapid increase in interest that many in the AI space are still unaware of.

DeepSeek's approach to AI training reflects a broader trend in the industry where companies are increasingly focused on optimizing both training and inference processes to enhance model performance.

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

We designed a system for both training and inference, not just a chip for training, to handle large problems effectively.