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

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 is a paradigm within machine learning that emphasizes multiple steps of computation rather than a single step, which is essential for achieving true intelligence.

The diminishing returns in pre-training mean that significant increases in intelligence will require exponentially more compute resources, making it harder to achieve breakthroughs.

DeepSeek optimized its models for efficiency by using 8-bit floating point format for training, which significantly reduces memory usage without sacrificing performance.

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

DeepSeek's advancements indicate that there is potential for rebuilding the stack for optimizing GPU workloads, improving software at the inference layer, and developing AI-generated kernels, which is promising for AI applications in both consumer and B2B sectors.

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

There's a specialization in AI models, where reasoning models that require more compute time are becoming popular for tasks like math and coding, while for consumer products, latency and intelligence per cost are prioritized.