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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…
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Why NVIDIA AI Accelerators Perform Best with Intel Xeon CPUs vs AMD EPYC
As AI adoption accelerates across industries, the choice of hardware becomes critical in optimising performance and efficiency. While NVIDIA AI accelerators like the H100, H200, and the recently announced B200 are leading the charge in AI workloads, their performance is not determined by the GPU alone. The CPU plays a crucial role in maximising throughput,…