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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,…
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Deploying AI Workloads in Virtualised Environments with VMware VVF, Private AI on VCF, RHEL KVM, and Nutanix
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),…