EXPERT SPEAK
WHY RIGHT-SIZING LLMS IS SO IMPORTANT FOR CHANNEL PARTNERS
Integrating Large Language Models into enterprise IT poses significant challenges. A critical success factor is the concept of right-sizing and developing LLMs of optimal size and configuration to match business needs. Channel partners, who play a role in technology implementation, struggle with this, says Ramprakash Ramamoorthy at ManageEngine, and provides a way forward.
The enterprise landscape is undergoing a transformation driven by rapid advancements in AI, particularly in the realm of Large Language Models, LLMs. In the United Arab Emirates and globally, businesses are increasingly aware of LLMs’ potential to revolutionise operations, from enhancing customer interactions to optimising data analysis. These AI systems, capable of understanding and generating human-like text, provide opportunities to boost efficiency, drive innovation, and gain a competitive edge.
Integrating LLMs into enterprise IT poses significant challenges. A critical factor for success is the concept of right-sizing, selecting or developing LLMs of optimal size and configuration to match specific business needs. Channel partners, who play a crucial role in technology adoption, often struggle with this.
At the heart of the current AI revolution are deep LLMs, trained on vast datasets, often with billions or trillions of parameters. Their architecture, based on transformer networks, allows them to perform a variety of natural language tasks such as generation, translation, and sentiment
Ramprakash Ramamoorthy, Director of AI research, ManageEngine analysis. While their scale contributes to powerful capabilities, it also brings challenges in terms of computational resources for training and deployment.
In contrast to these large, generalpurpose models, the concept of micro LLMs, or small language models, are specialised models designed for particular domains or tasks. Often fine-tuned versions of larger models, micro LLMs align more closely with industry or customer-specific needs. These models offer advantages like improved domain accuracy, lower computational costs, reduced latency, and enhanced data privacy via local deployment.
For channel partners, understanding and utilising a buy-and-build strategy, combining foundational models with finetuned, smaller models, can deliver rapid implementation and lower risks. LLMs can also be categorised based on their training and use cases, including general-purpose, instruction-tuned, and dialog-tuned models.
The concept of right-sizing LLMs is paramount for enterprises aiming to leverage this technology effectively. It involves selecting models that balance
By integrating rightsizing into their offerings through AI-readiness assessments, channel partners can deliver continuous support.
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