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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Why Enterprises Are Moving Workloads Back to On-Premise Private Cloud for Next-Gen AI

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There has been a clear trend in enterprise IT strategies in recent years, as firms are progressively moving workloads from the public cloud back to on-premise private clouds. The phenomenon known as “cloud repatriation” is particularly noticeable when it comes to advanced workloads such as artificial intelligence (AI) and machine learning (ML). In these cases, […]

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Harnessing the Power of VMware’s Private AI on Intel: Enhancing Data Security, Privacy, and Virtualising AI Workloads

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As artificial intelligence (AI) continues to transform workplace applications, the requirement for strong, secure, and efficient infrastructure becomes more and more crucial. VMware’s Private AI on Intel provides a compelling solution that enables the utilisation of AI in mainstream infrastructure. It focuses on ensuring data protection, privacy, and the smooth integration of AI workloads with […]

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