The AI landscape has been dominated by Large Language Models (LLMs)—massive neural networks trained on trillions of tokens, spanning hundreds of billions of parameters. These models, such as GPT-4 or Claude, have shown remarkable general-purpose intelligence, but they come with steep costs: enormous compute requirements, GPU dependency, and operational overheads that make them inaccessible for […]

Read More

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. […]

Read More

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 […]

Read More

Artificial Intelligence (AI) is transforming industries, but deploying AI workloads efficiently remains a challenge. Many organisations look to virtualisation to maximise resource utilisation, improve security, and streamline AI infrastructure management. This blog explores how to deploy AI workloads in virtualised environments using VMware Virtualised vSphere for AI (VVF), Private AI on VMware Cloud Foundation (VCF), […]

Read More