An Intellyx Brain Candy Brief
The vendors of publicly available ‘frontier’ large language models (LLMs) train them to be general purpose. As a result, they are expensive to host and are more likely to hallucinate than specialized models.
To address the limitations of such models, Adaptive ML leverages reinforcement learning to tune LLMs, delivering specialized models that are more likely to meet customer needs than the frontier LLMs.
Adaptive ML customers use their own data to tune their models iteratively using Adaptive ML’s tools, yielding models that have dramatically better cost per performance ratios than competing, general purpose models.
Given the immense token cost to support the reasoning AI agents require when leveraging publicly available models, Adaptive ML gives its customers a path to cost-effective autonomy for their AI agents.
It is best suited for large enterprises who wish to run their models on their own infrastructure, either in virtual private clouds in one of the hyperscaler clouds, or occasionally on-premises.
Copyright © Intellyx BV. Intellyx is an industry analysis and advisory firm focused on enterprise digital transformation. Covering every angle of enterprise IT from mainframes to artificial intelligence, our broad focus across technologies allows business executives and IT professionals to connect the dots among disruptive trends. None of the vendors mentioned in this article is an Intellyx customer. No AI was used to produce this article. To be considered for a Brain Candy article, email us at pr@intellyx.com.
