What are Large Language Models (LLMs)?

A Large Language Model (LLM) is a type of deep learning model designed to understand, generate, and manipulate human language. Built using transformer architectures, which were introduced in 2017, these models learn patterns from vast amounts of text data, enabling them to perform a wide range of tasks with impressive accuracy. LLMs, like GPT, are trained on massive datasets, which allows them to perform a wide range of complex language tasks. 

What makes LLMs stand out is how quickly and efficiently they can process text. Instead of working through words one at a time like older models, transformer-based LLMs look at entire sentences simultaneously. This helps them understand context and meaning in a much more natural way. With billions of parameters—essentially the internal settings the model uses to improve its accuracy—LLMs continuously get better at recognizing how words fit together to form coherent thoughts. These models have become foundational tools for applications across various fields, helping businesses automate customer service, create personalized content, and develop specialized AI systems for tasks in education, marketing, and beyond.

How do Businesses Benefit from LLMs?

Large Language Models (LLMs) are helping businesses work smarter, faster, and more efficiently. Here’s how they’re making a difference:

  • Boosting Efficiency: LLMs take over repetitive tasks like data analysis and customer support, giving employees more time to focus on important, strategic work.
  • Better Decision-Making: LLMs can sift through mountains of data and find insights that help businesses make smarter decisions, whether it’s understanding customer needs or spotting new trends.
  • Transforming Unstructured Data: One of the most valuable features of LLMs for businesses is their ability to convert unstructured input, like human input or scanned documents, into structured data. This means businesses dealing with heavy email or paperwork can speed up processes by automatically transforming these inputs into organized database records—without needing complex validation rules, text parsing, or OCR processing.
  • Easy to Scale: As businesses grow, LLMs can easily handle more work without needing extra resources, keeping things running smoothly without adding extra costs.
  • Improving Security: LLMs help protect businesses by spotting unusual patterns and predicting security threats, making it easier to prevent issues like fraud.
  • Saving Costs: By automating complex tasks, LLMs reduce the need for manual work, helping businesses save money while keeping operations efficient.

Overall, LLMs are making businesses more efficient, helping them make smarter choices, and streamlining processes by transforming unstructured data into useful, structured information—all while saving time and money.

Implementing LLMs Locally: Challenges and Solutions

Running Large Language Models (LLMs) on local infrastructure can seem appealing at first, offering potential control over data and deployment. However, as we discovered in our own attempts, reality often presents a host of challenges, particularly resource constraints and response times.

Testing LLMs on Local Machines

In our case, we decided to test the feasibility of running an LLM locally. We selected a smaller model (~7 GB) from Hugging Face and used the LlamaSharp library to interface with it. Our goal was to see if we could run the model on a standard development machine and get useful results without relying on cloud-based resources. The project was simple to set up and is even hosted on GitHub.

To try it yourself, follow these steps:

  1. Clone the repository: GitHub – Sipod Software
  2. Download the model from Hugging Face: DeciLM-7B
  3. Copy the model file system path in the “modelPath” variable in Program.cs (line 12).
  4. Run the application and start “chatting” with the model.

Performance Challenges

While the setup worked, we quickly encountered a significant limitation: we found out that we needed a lot more resources to get a response in an acceptable time. It took from 6 to 30 seconds for the app to get a full response from the model. The development machine we used, which had integrated graphics and heavily loaded CPUs, was clearly struggling to handle the model efficiently.

This kind of delay might be acceptable for development purposes, but it is far from ideal for real-time applications. For businesses, this means that running LLMs locally—especially at scale—requires hardware beyond what is available in most standard setups.

Turning to Cloud Solutions

After facing these performance issues, we realized that running LLMs locally wasn’t going to provide the speed or scalability we needed. Cloud solutions became the obvious choice, offering the computational power necessary to deliver fast, accurate results without overburdening local resources. By moving to the cloud, businesses  can access the full potential of LLMs without upfront hardware investments.

Key Model Hosting Services: fal.ai & replicate.com

When exploring cloud-based solutions for hosting Large Language Models (LLMs), two standout services emerged: fal.ai and replicate.com. Both platforms provide access to a wide range of hosted models via REST APIs, making it easy for businesses to integrate AI capabilities into their workflows without the need for local infrastructure.

Why fal.ai & replicate.com?

Internally, we needed to test image generation using the new Flux model, and both fal.ai and replicate.com proved to be excellent choices. The integration process was straightforward, as all we had to do was implement REST API calls to connect with these platforms. This ease of use allowed us to quickly develop and deploy a small web application that integrates with both services. You can check out the app here. To get started, you’ll need to obtain a token from either fal.ai or replicate.com to access their models.

More Than Just Image Generation

While our primary focus was on image generation, these platforms offer far more. From text generation and code generation to optical character recognition (OCR), fal.ai and replicate.com provide versatile model options, enabling businesses to explore various AI-driven solutions tailored to their specific needs.

The Future of LLMs in Business Applications

Large Language Models (LLMs) are becoming essential for businesses looking to automate complex tasks, gain deeper insights from data, and streamline everyday operations. Whether it’s transforming customer service or organizing unstructured data, LLMs provide real, tangible benefits that help businesses work smarter.

However, running LLMs locally can be tricky due to the need for significant computing resources. That’s where cloud solutions like fal.ai and replicate.com come in, making it easier for companies to access powerful models without hefty infrastructure investments. As we continue to experiment with LLMs, these tools are showing immense potential to handle real business challenges.

By integrating LLMs, businesses can not only improve efficiency but also explore new opportunities for growth and innovation, staying competitive in an increasingly digital world.