Large Language Models – itsabout.ai

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. […]

Simon Todd in 05 Jun 2025 No Comments

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 […]

Simon Todd in 07 Apr 2025 No Comments

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 […]

Simon Todd in 08 Mar 2025 No Comments

Why RAG is Important vs Fine-Tuning and Training

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In the swiftly changing realm of artificial intelligence, companies are pursuing the most effective methods to optimise Large Language Models (LLMs) for their specific needs. Although conventional techniques like fine-tuning and comprehensive training are prevalent, Retrieval-Augmented Generation (RAG) is developing as a more efficient and pragmatic alternative. This essay will examine the significance of RAG, […]

Simon Todd in 01 Feb 2025 1 Comment