A site dedicated to Artificial Intelligence
One of the most frequent questions I’m asked by customers embarking on AI projects—whether it’s training deep learning models, running inference workloads at the edge, or scaling machine learning in a hybrid environment—is: “How easy is it to deploy the tools?” The answer often surprises them. While NVIDIA has been the dominant player in AI […]
In recent years, the crypto “energy crisis” sparked global alarm. Bitcoin mining alone consumed roughly 0.4% of global electricity, and crypto‑mining + data centers already made up about 2% of world demand in 2022 [1]. But now, AI workloads—particularly generative and large‑language‑model (LLM) operations—are poised to make an even bigger dent in our energy systems […]
As enterprises rapidly adopt AI to improve efficiency, customer experience, and innovation, the choice of model architecture has become a critical factor. Whether it’s deploying a massive Large Language Model (LLM), an efficient Very Large Language Model (VLLM), or a compute-friendly Small Language Model (SLM), organisations are increasingly strategic about balancing performance, cost, and accuracy. […]
Not long ago, I wrote about why Retrieval-Augmented Generation (RAG) is such a pivotal architecture in modern AI workflows, particularly when compared to fine-tuning and training from scratch. The core argument was simple: RAG enables models to stay up-to-date, grounded, and efficient without massive retraining costs. It was (and still is) a pragmatic solution to […]