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Are We Heading into Another Energy Crisis — This Time for 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…
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The Most Popular LLMs, VLLMs, and SLMs in Enterprise AI Today
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.…
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Is RAG Still Relevant in a Post-LLaMA 4 World?
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…