Category Archives: Uncategorized

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 →

Modern AI models are inherently inefficient because they work on the same task with the same level of intensity: they engage the same amount of compute for every question instead pulling static facts in. DeepSeek Engram (https://arxiv.org/abs/2601.07372) helps in solving … Continue reading →

A language model has to understand many things at the same time when it reads a sentence: word meanings, grammar, context, and world knowledge. Consider the sentence “The bank is by the river”. The model must simultaneously consider: The model … Continue reading →

If you’ve ever splurged on a processor or raved about a supercomputer, you’ve probably been talking about “Gigahertz,” “MIPS” or “TeraFLOPS.” We often imagine these numbers in terms of horsepower in a car, a given reading showing us how fast … Continue reading →

NVIDIA TiDAR, which stands for “Think in Diffusion, Talk in Autoregression”, is a hybrid architecture designed to make Large Language Models (LLMs) significantly faster without losing quality. Traditional models like GPT work like a person writing a letter one word … Continue reading →

A fundamental transformation is reshaping the hardware landscape, driven not by the familiar cadence of Moore’s Law, but by the physical realities of data movement. For the past decade, the dominant narrative in AI acceleration has been the relentless pursuit … Continue reading →