Latest from Vellex

AI models deployed on edge hardware degrade over time as field conditions change. The traditional solution, cloud retraining, is costly, consumes significant power, and relies on connectivity that remote systems lack. This article examines the compounding constraints of power, memory, and connectivity that have historically prevented on-device AI training. Discover how Vellex Computing leverages analog circuit dynamics to enable continuous, real-time parameter updates directly on edge hardware, removing cloud dependency for autonomous robotics, satellite platforms, and industrial IoT.

Analog vs. Digital technology is a decades-old debate, but which one is actually better for the future of tech? In this comprehensive guide, we break down the core differences between continuous analog waves and discrete digital steps. Discover the unique pros and cons of each system, learn which architecture is best suited for your specific industry—from professional audio to cutting-edge AI—and explore how modern hardware engineers are blending both into powerful, ultra-efficient Hybrid (A/D/A) systems to overcome the massive energy limitations of today's devices.

Tokens are the invisible currency of artificial intelligence, and processing them carries a staggering hidden cost. Why does training a frontier model require gigawatt-hours of electricity, while developers face unexpected API bills? Step inside this foundational metric to decode these massive economics. This deep dive breaks down the physical infrastructure required to process trillions of data fragments—from specialized GPU clusters to liquid cooling taxes. Discover the invisible multipliers inflating your usage, and learn comprehensive optimization strategies like smart routing and prompt caching to drastically reduce your AI costs.