The Most Popular LLMs, VLLMs, and SLMs in Enterprise AI Today

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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?

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

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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), […]

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Fine-Tuning Parameters for vLLM on Gaudi3, H200, and MI300X

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The rise of large language models (LLMs) has driven significant demand for efficient inference and fine-tuning frameworks. One such framework, vLLM, is optimised for high-performance serving with PagedAttention, allowing for memory-efficient execution across diverse hardware architectures. With the introduction of new AI accelerators such as Gaudi3, H200, and MI300X, optimising fine-tuning parameters is essential to […]

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