Article

Rethinking Generative AI: On-Premises, Open Source, and Multimodal

NEW OPPORTUNITIES TO SCALE UP AI

On-premises large language models, open source models, and multimodality open up a new avenue of opportunities for organisations of all sizes.

The Unwavering Momentum of the Generative AI Market

The past two years have witnessed continuous dynamism in the market, with the launch of hundreds of models, from the widely recognised large language models (LLMs) introduced by major vendors to domain-specific small language models, often built upon developer community-supported open-source solutions.

At the same time, various fine-tuning methods have evolved, ranging from additive fine-tuning to selective fine-tuning and reparameterised fine-tuning. Machine Learning Reply has extensively experimented with the full spectrum of alternatives available in the market and has developed a “2.0” approach based on three key pillars: on-premises models, open-source models, and multimodality.

INDEX

The Benefits of On-Premises Models

Organisations are increasingly adopting on-premises solutions to ensure GDPR compliance, safeguard sensitive data, and maintain full control over data storage and processing. These models provide complete data privacy, minimise breach risks, and are particularly valuable for highly regulated and privacy-sensitive industries such as healthcare, finance, and government.

On-premises solutions also help organisations meet strict sovereignty and data security laws by preventing exposure to third-party providers. They offer greater independence from “black box” services offered by AI vendors and allow models to be tailored to specific business needs. Additionally, for AI-driven applications with high request volumes, such as conversational systems, real-time translation tools, or recommendation engines, on-premises deployments ensure more predictable and efficient operations.

The High Value of a Hybrid Approach

On-premises deployments enable organisations to balance performance and cost while fine-tuning models for specific business needs. However, a fully on-premises setup requires investment in hardware, infrastructure, IT personnel, upgrades, scaling, and energy costs. To address these challenges, the market offers privacy-sensitive hybrid solutions.

A hybrid approach keeps sensitive, high-volume tasks on-premises while leveraging the cloud for low-frequency tasks, ensuring both privacy and cost efficiency. Private cloud virtual machines provide powerful hardware with configurable setups and no maintenance costs. This flexible approach allows businesses to adapt dynamically as needs evolve, enabling direct model deployment and a ready-to-go serverless setup in secure private cloud environments.

Open Source Meets AI

Open-source models are transforming the AI landscape by offering accessibility, customisation, and transparency, making advanced technology available to businesses of all sizes. In fact, they eliminate barriers to entry for start-ups and small companies by providing cutting-edge capabilities without requiring significant capital investment or incurring high usage fees.

Open-source models also foster rapid innovation, allowing organisations to modify and enhance existing solutions without relying on third-party players for updates. Their transparency ensures compliance with industry regulations and ethical standards, which is particularly crucial in fields like pharmaceuticals and legal services. Additionally, open-source AI reduces the risk of vendor lock-in, giving businesses greater control over their technology, scalability, and integration with other tools.

Adapting and Customising the Models

Large language models are powerful tools trained on diverse datasets, but their general-purpose nature may not always meet the specific needs, terminologies, or workflows of particular industries. Adaptation of open source models involves tailoring them to enhance their performance and relevance for specialised tasks or domains, ensuring more accurate and context-aware outputs.

Personalising an LLM helps organisations align the model with specific requirements, improving efficiency and unlocking superior performance. This is particularly important not only for enhancing domain-specific expertise, but also for focusing on task-specific performance such as summarisation or sentiment analysis, adapting to evolving knowledge, and addressing the limitations of generic models, which may be trained on outdated or incomplete data, thus reducing errors in niche scenarios.

The Future of Generative AI is Multimodal

Multimodality enables generative approaches to be applied to a range of domains beyond text, including scenarios where the primary data sources are images, video, and audio, as well as the interpretation of graphics and visual content in documents or from the physical world.

Developing multimodal models involves combining unique traits from various data sources with large benchmark datasets, which is essential for creating compelling open models applicable to various business areas. This approach opens up new possibilities for integrating multiple forms of data to improve performance and versatility across different industries.

Multimodality Works Better with Open- Source and On-Premises Solutions

Advanced multimodal AI models, particularly those processing audio and video, often require significant computational resources per call. For organisations that use these models frequently, the costs associated with pay-per-use service models can become prohibitive, making on-premises solutions a more economical option.

In fact, on-premises deployments of open-source multimodal AI models with high compute demands are cost-effective, as they avoid per-call proprietary cloud expenses. Therefore, open-source multimodal models are particularly useful for organisations that require frequent requests, have low fault tolerance, need specialised models for diverse data sources, or require strict low-latency or edge computing deployment capabilities.

Exploit the Opportunities of Generative AI 2.0

Machine Learning Reply has developed robust methodologies and frameworks that enable organisations to move beyond the standard "as a Service" models and create highly customised AI solutions. By leveraging the advantages offered by open-source models and on-premises setups, Machine Learning Reply empowers businesses to build tailored and multimodal systems that align with their unique needs and goals.

These solutions allow for greater control over the deployment and fine-tuning of models, ensuring they meet specific requirements in terms of performance, security, and data privacy. Machine Learning Reply's approach facilitates integration with existing workflows, provides flexibility to adapt to evolving business demands, and reduces reliance on third-party services, ensuring long-term scalability and resilience.

Machine Learning Reply

Machine Learning Reply is the Reply group company specialised in Machine Learning, Cognitive Computing and Artificial Intelligence solutions. Machine Learning Reply, based on the most recent developments in the field of artificial intelligence, applies innovative Generative AI, Deep Learning, Natural Language Processing, Image/Video Recognition techniques to different usage scenarios such as smart automation, predictive engines, document processing, recommendation systems and conversational agents.