Monthly Archives: February 2026

For decades, Convolutional Neural Networks (CNNs) have been the undisputed kings of computer vision. If a machine was “seeing,” it was likely using a CNN. But the landscape is shifting. Vision Transformers (ViTs) are moving from the world of Natural … Continue reading →

Modern generative AI is hitting a familiar wall: every time we try to make models “smarter” by stuffing in more knowledge, we also make them more expensive to run. In classic dense Transformers, memory and compute are tightly coupled, more … Continue reading →

The rapid shift of language models from research artifacts to production-critical systems has forced a deep re-evaluation of how inference workloads are architected. Early optimization efforts focused almost exclusively on training, where performance is dominated by dense matrix multiplications and … Continue reading →