The surge of commercially available pretrained AI models in recent years has reshaped the business landscape, marking a transformative shift in how organizations approach AI adoption. In part one of this series, we explored the rise of inferenceāthe ability to leverage trained AI models for hyper-fast insights and decisions based on new data. As the business focus turns increasingly toward inference, pretrained models are becoming a major focus.
Pretrained models have democratized AI, making AI accessible to organizations across industries without requiring them to invest in extensive training infrastructure. From customer support to fraud detection, talent acquisition, and supply chain optimization, these models have introduced AI capabilities that were previously the domain of only a few tech giants.
In this blog, weāll examine the current state of pretrained models, the trends shaping their development, and how they are set to drive AI adoption and innovation in 2025. Weāll also explain how your business can get easy access to them via the Gcore Inference at the Edge model library.
The current state of pretrained models
Pretrained models are AI systems trained on vast datasets using complex architectures, enabling them to perform tasks like natural language understanding, image recognition, and real-time data analysis. These models are typically deployed with ongoing monitoring and fine-tuning to make sure they meet evolving performance demands.
Key examples of pretrained models include:
- GPT: Widely recognized for its generative AI capabilities, GPT enables content creation, conversational AI, and automated insights.
- BERT: Developed by Google, BERT excels in understanding text and is widely used in tasks like search optimization and sentiment analysis.
- CLIP: A neural network model that links images and text, enabling tasks like content moderation and visual search.
You can try out a number of pretrained models on the Gcore Inference at the Edge Playground.
These models have revolutionized industries, democratizing access to applications such as intelligent chatbots (GPT), advanced text analytics (BERT), and image-based content moderation (CLIP). In 2024, 75% of organizations have increased investments in data lifecycle management due to advancements in generative AI, showing the massive potential and realization of pretrained AI models.
However, challenges remain. Only 45% of organizations feel fully prepared to integrate pretrained models into their infrastructure, and only 41% are confident in their data management capabilities. So what does the future look like for pretrained models, and how will it address these ongoing challenges for businesses?
Trends and projections for pretrained models in 2025
To fully harness the potential of pretrained models, significant advancements in infrastructure, technology, and governance are needed. Here are key trends to watch.
1. Sector-specific pretrained models
In 2025, pretrained models will be increasingly designed for specific industries, enabling businesses to unlock AI capabilities tailored to their unique challenges and opportunities. Hereās how this trend will manifest across key sectors:
- Finance: Pretrained models will improve risk assessment by analyzing financial transaction data in real time to detect anomalies indicative of fraud. For example, banks could use these models to flag unusual spending patterns or identify suspicious wire transfers automatically. Models will also be used to enhance credit scoring systems, making loan approvals faster and more accurate, especially for underbanked populations.
- Healthcare: Diagnostic tools powered by pretrained models will help identify diseases earlier and more accurately. For instance, an AI trained on millions of radiology images can assist radiologists in detecting early signs of conditions like cancer or lung diseases. Patient monitoring systems can leverage AI to analyze vitals in real time, alerting healthcare professionals to potential emergencies, such as a sudden drop in blood oxygen levels.
- Retail: Personalized recommendation engines will become more intuitive and context-aware. Imagine a retail chain using AI to predict individual customer preferences during holiday seasons based on historical purchasing behavior and real-time browsing data. Inventory optimization models will also help retailers reduce waste by predicting demand spikes and adjusting stock levels dynamically across locations.
These advancements will lower costs by enabling businesses to fine-tune pretrained models with smaller, domain-specific datasets, speeding up deployment.
2. AI model as a service (MaaS)
The concept of AI Model-as-a-Service (MaaS) is poised to transform AI accessibility for businesses of all sizes in 2025. By providing access to pretrained models via subscription or API-based systems, MaaS eliminates the need for significant investment in hardware or the hiring of specialized talent. This shift will allow smaller companies and startups to leverage advanced AI tools, leveling the playing field and fostering innovation.
For example, a small e-commerce business could integrate an AI-powered recommendation engine within days, enabling personalized customer interactions without requiring in-house expertise. Similarly, a tech startup could deploy a state-of-the-art chatbot to handle customer inquiries seamlessly, cutting the time needed for development.
The simplicity of MaaS encourages rapid prototyping and experimentation, allowing businesses to deploy AI solutions tailored to their needs without the traditional overhead associated with model training and maintenance.
3. Governance and ethical AI
As AI becomes deeply embedded in business operations, the importance of governance and ethical considerations is growing exponentially. In 2025, stricter compliance requirements will require AI systems to align with privacy laws, intellectual property regulations, and societal values. These governance measures will build trust in AI systems so they as responsible and equitable as they are innovative.
For instance, companies using AI for customer data analysis must demonstrate that their systems comply with regional privacy laws, such as GDPR in the EU or emerging AI-specific regulations in the US. Bias mitigation will also be a critical focus, with organizations expected to audit pretrained models to ensure fairness in applications like recruitment or lending decisions.
Explainability will become a cornerstone of governance efforts. Businesses will need to make AI decisions transparent to stakeholders, providing clear, comprehensible justifications for outcomes, such as why a certain credit score triggered a loan denial.
4. Transfer learning and few-shot learning
Advances in transfer learning and few-shot learning will make pretrained models more versatile and adaptable, opening up new opportunities:
- Transfer learning: By transferring knowledge from one task to another, pretrained models will enable businesses to adapt solutions faster. For example, a logistics company could fine-tune an AI model trained on general weather patterns to predict localized delivery disruptions.
- Few-shot learning: Few-shot learning will empower AI to generalize from minimal data. In industries like security, few-shot learning could be used to identify new cyber threats by analyzing just a few examples of previously unseen malware. Similarly, in retail, a few-shot model might create accurate product recommendations based on a limited dataset of customer interactions for a newly launched product.
These advancements will reduce data requirements, enabling organizations with limited resources to adopt cutting-edge AI solutions efficiently.
Technological advancements driving pretrained models
The evolution of pretrained models depends heavily on innovation in hardware and infrastructure. For example, NVIDIAās H200 GPU, with its increased VRAM and enhanced processing capabilities, is setting new benchmarks for model deployment. These hardware upgrades enable faster fine-tuning and inference, critical for real-time applications.
By processing data closer to its source, edge computing accelerates the deployment of pretrained models, reducing latency and enhancing efficiency. This is particularly valuable in industries like retail, where real-time AI applications such as demand forecasting and personalized recommendations are critical.
Leverage Gcore for pretrained models
Our Edge AI solutions are designed to help businesses seamlessly adopt pretrained models without the complexity of building AI from scratch. By leveraging high-performance NVIDIA GPUs, we empower faster deployment, reduced setup times, and lower costs for AI training and inference alike.
With a vast model library available directly in the Gcore Customer Portal, weāre on a mission to make powerful inference technology intuitive to use. Whether youāre looking to deploy chatbots, fraud detection systems, or recommendation engines, weāve got the infrastructure you need to implement pretrained models at scale.
Get a personalized consultation to explore inference with Gcore
