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 running incredibly large neural networks with high performance and low power consumption, requiring different solutions for training and inference workloads.

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.

You need big models to train smaller models effectively, and while we have efficient smaller models, they often rely on insights from larger models.

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

We've applied AI to various tasks, including chip design and scheduling, making processes more efficient, but it remains constrained to domains that are provably correct, like mathematics and coding.

Currently, we train bespoke models for each problem, but the goal is to develop models that can generate solutions for multiple problems, enhancing architecture search.

Our high-performance chip system, called a pod, consists of 256 chips capable of 11.5 petaflops of compute, enabling us to tackle larger problems.

We made these chips programmable via TensorFlow, allowing models to run on CPUs, GPUs, or TPUs with minimal modifications.