Training and inference are fundamentally different computational problems. The hardware optimal for one can be entirely wrong for the other. Misunderstanding the differences between CPUs and GPUs can cost companies thousands. In this post you’ll learn how to make better architectural decisions around chips.
Random Sampling Is Sabotaging Your Models!
Did you know diversity can detect hallucinations? Ask an LLM “what’s the capital of France” five times and you’ll get some variation of “Paris” over and over again. But ask that same LLM about the “boiling point of a dragon’s scale” and you’ll get five different made-up answers.
When State Space Models Learned to See Globally 👓
Mamba promised linear time complexity. Vision benchmarks said “prove it”, and pure Mamba models fumbled badly. Transformers kept winning until MambaVision showed up and rewrote the rules entirely 🐍👓.