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Understanding large language models (LLMs)

catalyst usecase author
Srilakshmi | Technical Writer

October 15, 2025

Inside the Cookbook

  • What is a large language model (LLM)?
    • Key applications of LLMs
      • What is LLM serving, and why does it matter?
        • The role of LLM serving
      • Architecture of LLM serving
        • QuickML: Making LLM serving easy
          • Available models in QuickML's LLM serving
            • Breakdown of QuickML’s chat interface
              • Key parameters you can control
            • Integrating LLMs into your applications

              What is a large language model (LLM)?

              • A large language model (LLM) is an advanced AI system trained to generate human-like text based on patterns learned from vast datasets (such as books, websites, and articles). LLMs use deep learning techniques that process entire sequences in parallel.

              • LLMs typically learn through unsupervised or self-supervised learning, predicting the next word in a sentence by recognizing patterns, grammar, and context.

              • Think of your phone’s autocorrect or predictive text feature. When you start typing “Happy birth,” your phone often suggests “Happy birthday” because it learned from patterns in your previous messages and from common language usage.

              • LLMs work in a similar way on a much larger scale. Instead of learning from just your texts, they’ve learned from billions of sentences across books, websites, and articles. That’s why they can predict and generate text that feels natural and context aware.

              • These models process text in units called tokens. A token is a small chunk of text often a word or part of a word that the model uses to understand and generate language. For example, the word “Happy” is one token, while “birthday!” might be split into two tokens: the word "birthday" and the exclamation point. LLMs work with tokens instead of characters or whole sentences because it helps them handle language more efficiently.

              Key applications of LLMs

              LLMs are unlocking new possibilities across industries. Here are some of the most impactful applications:

              • Chatbots and virtual assistants: Automating customer support, ecommerce assistance, and enterprise help desks

              • Content generation and summarization: Creating blogs, reports, and product descriptions or summarizing lengthy documents

              • Code generation and debugging: Assisting developers with writing and optimizing code in multiple languages

              • Language translation and localization: Providing real-time, context-aware translations for global businesses

              • Image and text analysis (multimodal AI like Pixtral-12B): Processing both text and images for industries and accessibility

              What is LLM serving, and why does it matter?

              Building and training a large language model is only half the journey. The real challenge begins when you need to make that model accessible, reliable, and efficient for real-world applications. This process is known as LLM serving.

              In simple terms, LLM serving is about deploying the model so it can handle real-time user queries at scale. It’s what turns an AI prototype into a production-ready solution.

              The role of LLM serving

              LLM serving is essential because it:

              • Brings AI to production: Transforms a static, trained model into a live service ready for real use cases

              • Handles scale: Supports high volumes of simultaneous requests without compromising speed or accuracy

              • Enables easy integration: Fits seamlessly into applications, chatbots, enterprise tools, and digital workflows

              • Drives business impact: Converts cutting-edge research into practical, customer-facing solutions that create value

              Architecture of LLM serving

              A robust LLM serving system typically includes:

              • Client layer: Handles user requests

              • API layer: Translates requests into a model-readable format

              • Model layer: Processes queries using the LLM

              • Data layer: Manages input and output data flow

              QuickML: Making LLM serving easy

              Many platforms offer LLM access, but QuickML takes it to the next level with simplicity, flexibility, and enterprise readiness. Here’s why QuickML stands out:

              • Fine-tune responses effortlessly: Adjust creativity, tone, and length.

              • Seamless model switching: Use multiple models in one interface.

              • Effortless integration: Deploy LLMs into apps with API endpoints.

              • Optimized performance and cost control: Reduce token usage.

              • No complex setup: Accessible even for non-technical users.

              Available models in QuickML's LLM serving

              QuickML’s LLM serving brings you a curated suite of advanced models, each engineered for maximum impact in its domain.
              These models aren’t generic; they’re purpose-built to deliver speed, accuracy, and efficiency:

              • Qwen 2.5 - 14B Instruct: Great for general-purpose tasks like Q&A, summarization, and content generation

              • Qwen 2.5 - 7B Coder: Specially designed for programming and debugging

              • Pixtral 12B: A multimodal model that understands both text and images

              Whether it’s generating content, writing flawless code, or interpreting complex visual data, QuickML ensures you have the right intelligence for the right task.

               

              Breakdown of QuickML’s chat interface

              • Model selection: Switch between models easily.

              • Model details: View size, token limits, and integration options.

              • Parameters panel: Adjust temperature, top-k, top-p, and max tokens for custom responses.

              • Chat panel: Type your queries and interact in real time.

              Key parameters you can controlKey parameters you can control

               

              • Temperature: Control creativity and precision.

              • Top-K and Top-P: Manage randomness and diversity.

              • Max tokens: Define response length.

              • Custom instructions: Ensure domain-specific tone and compliance.

              Integrating LLMs into your applications

              QuickML provides OAuth-secured API endpoints for embedding LLMs into apps, CRMs, or websites. With ready-to-use code snippets, integration becomes smooth and secure.

              QuickML's LLM serving: Currently in early access

              If you want to explore LLM serving for your business, request early access today by contacting our support.

              No hassle. No complex setup. Just choose a model, configure parameters, and start interacting.