Deploying AI Without the Bloat: The Rise of Distilled LLMs (Part 1 of 2)

Introduction

?

The rise of Large Language Models (LLMs) has revolutionized AI, unlocking unprecedented capabilities in natural language processing and beyond. However, their deployment in real-world applications is often hindered by substantial resource demands, limiting their practicality in environments with constrained computational power, like mobile devices and edge computing platforms. This limitation poses a significant challenge for the widespread adoption of AI agents, particularly in scenarios requiring low latency and high efficiency.

Enter Knowledge Distillation, a powerful technique that addresses these challenges by transferring the 'knowledge' from a large, complex LLM (the 'teacher') to a smaller, more efficient model (the 'student'). This process not only reduces the resource footprint but also enables AI agents to operate in diverse and resource-limited settings, opening up a plethora of new applications. This article delves into the intricacies of knowledge distillation, exploring how it empowers AI agents to overcome resource limitations and expand their operational horizons.

Meet the Author

CSM Architect | Generative AI & Hybrid Cloud Strategist | Enabling Digital Transformation

Author Sireesha Ganti is a CSM Architect & Technical Specialist at IBM. She has background and expertise in working with clients across multiple domains implementing & designing solutions for facilitating digital transformation. Sireesha specializes in generative AI, automation technologies, and their practical applications, combining her passion for learning with technical writing, solution design and implementation. She is currently driving AI adoption, application modernization, and business automation for enterprise clients.

Challenges with running AI Agents:

While LLMs are highly accurate and demonstrate capabilities like in-context learning, there are certain challenges in their applicability to a wide range of use cases. For example, during deployment, LLMs are faced with high resource and memory demands, increased latency, and decreased throughput.

To put things in perspective, a single 175 billion parameter LLM requires about 350 gigabytes of GPU memory. Smaller LLMs with about 10 million parameters still require 20 gigabytes of GPU memory.

As we uncover newer use cases for AI agents, it is becoming evident that agents will need to operate in environments with limited computational & memory capabilities, which include agents running on mobile devices, or on edge computing platforms/devices.? LLMs require substantial memory & processing capabilities and that can be very impractical for these settings.

For example, smart cameras or autonomous vehicles where the input data must be processed and tasks executed locally on the device and in real-time. These are scenarios where low latency & resource efficiency become crucial.

One significant challenge that distilled models are solving for AI agents is resource efficiency. This is where knowledge distillation of large language models comes into play. So, how does it work?

First, what is Knowledge Distillation?

Knowledge Distillation is a deep neural network methodology that involves transferring the[JM1]?[JM2]?knowledge from a large, pre-trained LLM with billions of parameters (known as the "teacher") to a smaller, simpler model (known as the "student").?

That’s how the AI agents that use student models will have the ability to operate on a wider range of devices & platforms where traditional LLM-based agents would not be the best choice.

So, what does the Teacher know?

The teacher model is an LLM based on transformer architecture and has already been pre-trained. This means that the weights or parameters from across all the neural network layers of the teacher LLM have been adjusted and optimized. The optimization is an iterative process where the prediction errors are minimized continuously across massive amounts of training data until a point where the model converges. Think of convergence as a point during the training at which the LLM has effectively learned all the patterns and relationships hidden in the training data. This is where the LLM’s performance stabilizes i.e., any further iterations to adjust the weights or parameters will not cause the model to yield better results on predictions of next token.

Now, we have a pre-trained teacher model whose knowledge of the vast training data, in many ways, is encoded in the form of its weights or parameters. The teacher model excels at generalizing i.e., predicting the next token when exposed to new & real-world data.

And, what does the Student learn from the Teacher?

The teacher is pre-trained but the student model is yet to be trained and it will use the knowledge of the teacher to train and learn from during the distillation process. There are some teacher-student architectures where they both train together, but we will get to that later.?

So, in many ways, the knowledge from the teacher model gets compressed into a smaller student model during the distillation process.? Note that the student model is training on outputs of teacher model and NOT the original dataset that teacher model trained on.

DeepSeek-R1 and DeepSeek-V3 are recently popularized distilled models.? Few other distilled models and their teacher models are shown here:

A Primer on Transformer Architecture:

For example, a panel of judges in a talent show convert the raw scores of participants into percentages. The percentages are better indicators of which participant is likely to win. This “normalization” gives the judges a better perspective on who is winning.

?

How does the Knowledge Distillation work?

The student learns the teacher’s “knowledge” in 3 main ways:

1.???? Response based: student learns/trains from the raw or un-normalized outputs of the teacher’s final neural network output layer (which is a neural network layer).?

2.???? Feature based: student learns from outputs of the teacher’s intermediate neural network layers (also known as "hints”)

3.???? Relationship based: student learns the relationships between different data points as learned by the teacher.

?

This illustration shows different areas where the knowledge is located in the teacher’s neural networks and how that corresponds to student’s learnings. (Source: Arxiv)

?

?

Benefits of Distilled Student model

Distilled models work because the knowledge of large teacher models is compressed into smaller and more efficient versions i.e., student models. Key benefits of student models include:

• Reduced Memory Footprint: Distilled models use less memory than their teacher counterparts. Application: This makes them suitable for deployment on devices with limited random-access memory (RAM).
• Lower Energy Consumption: Distilled models are known to consume less power during inference. Application: This capability is crucial for mobile phones and other devices that operate on limited power supply i.e., batteries.
• Faster Processing Times: Distilled models can process data much quicker than their teacher counterparts. Application: This makes them an enticing choice for AI agents because such agents are highly responsive in time-critical situations.

?

For Example:

Imagine a virtual assistant agent running on a smartphone. Using a distilled model as the cognitive engine for this agent allows the agent to understand and respond to the user’s commands optimally without draining the device battery or worse failing at the task at hand because the traditional LLM-based agent requires consistent internet connectivity to access powerful cloud-based models. This agent can run locally on the device with limited resources. Fantastic, isn’t it!

By solving the resource efficiency challenge, distilled models enable AI agents to deliver high-quality performance in a broader range of applications and environments.

Key Applications Areas for Agents using Distilled Models:

Knowledge Distillation is revolutionary because it opens doors to scenarios where teacher model has the knowledge of a certain modality (such as vision or image), which can be distilled into a student model to generate outputs for a different modality (text or speech). The range of use cases here is vast.

1.???? Visual Recognition: Agents using distilled models are used for creating highly accurate classification of complex images and facial recognition from low resolution pictures. Other applications include lane detection, object detection, pedestrian detection, video captioning, landmark detection, text-to-image synthesis, etc.

2.???? NLP: Distilled model-based agents are used in natural language processing applications especially to perform multi-lingual tasks where knowledge from multilingual teacher models can be distilled into student models.

3.???? Speech Recognition: Distilled model-based agents are able to provide accurate speech recognition across different languages and accents with features such as speech language identification, audio classification, accent detection, speech synthesis, speaker recognition, etc.

Is Knowledge Distillation same as Supervised Fine Tuning of LLMs?

The answer is somewhat yes and no. During supervised fine-tuning, a pre-trained LLM is trained further on data specific to domain, application, or task in order to enhance that LLM’s capabilities in that specific area of expertise. The fine-tuned model is usually the same size at its prior version.?

On the other hand, knowledge distillation involves transfer of embedded knowledge from a pre-trained teacher model to a smaller student model where the student learns to replicate the teacher’s outputs. In both cases i.e., supervised fine-tuning and knowledge distillation, the weights or parameters of the model are updated.

The Analogy

To understand this difference better, consider a veteran artist who is a master painter and has enough knowledge to teach someone. This painter teaches a novice (student painter) all the techniques and intricacies while also providing the novice with feedback. The novice student painter learns to replicate the teacher’s style and techniques efficiently while producing high-quality paintings. This is knowledge distillation based on teacher-student model.

On the contrary, if the novice painter, who is already trained, but wants to specialize in landscape painting, then they train on techniques specific to that style and receive feedback from the teacher along the process. This is supervised fine-tuning.

Conclusion

Knowledge distillation emerges as a pivotal solution for deploying high-performing AI agents in resource-constrained environments. By effectively compressing the knowledge of large, complex LLMs into smaller, more efficient student models, we unlock a multitude of applications previously deemed impractical. The benefits, including reduced memory footprint, lower energy consumption, and faster processing times, are undeniable. From virtual assistants on smartphones to real-time processing in autonomous vehicles, distilled models are paving the way for a future where AI agents are seamlessly integrated into our daily lives. While distinct from supervised fine-tuning, knowledge distillation provides a unique and powerful pathway to optimize LLM performance, ensuring that the transformative potential of AI is accessible across a broader spectrum of devices and applications. As we continue to explore the vast possibilities of AI, knowledge distillation will undoubtedly remain a cornerstone in the evolution of intelligent systems

Getting Started

If you're looking to integrate LLMs into AI agents using IBM solutions, here’s how you can begin:

1?? Define the Role of Your LLM-Agent – Will it be an advisor, decision-maker, or fully autonomous agent? Clearly defining its role will help in selecting the right architecture.

2?? Leverage IBM Watsonx.ai for LLM Integration – IBM’s Watsonx.ai provides a powerful platform to deploy, fine-tune, and scale large language models (LLMs). While Watsonx.ai itself is not an agent-building tool, it serves as the cognitive layer that can be integrated into AI agents to enhance reasoning, natural language understanding, and decision-making.

3?? Implement Context & Memory Management with Watson.data and Milvus – LLMs require efficient context management. Use IBM Watson.data for structured data storage and Milvus for managing vector databases to enable retrieval-augmented generation (RAG), ensuring agents retain knowledge over time.

4?? Enhance Real-World Interaction with Watsonx Orchestrate – IBM Watsonx Orchestrate enables AI agents to interact with enterprise applications, automate workflows, and execute tasks autonomously, serving as an orchestration layer for LLM-powered agents.

5?? Optimize & Govern AI Performance with IBM Watsonx.governance – To ensure AI compliance, fairness, and risk mitigation, leverage IBM Watsonx.governance to monitor and manage AI agent behavior, track decision-making processes, and ensure regulatory adherence.

?? Looking to build your own AI-powered agent? Start by integrating LLMs with Watsonx.ai, manage knowledge with Watson.data & Milvus, automate workflows with Watsonx Orchestrate, and ensure governance with Watsonx.governance.

References:

https://arxiv.org/pdf/2006.05525??

What is Knowledge distillation? | IBM??

https://labelbox.com/blog/a-pragmatic-introduction-to-model-distillation-for-ai-developers/??

?

Disclaimer

This article is written by?@Sireesha Ganti and published in the?Gen AI Trends & Applications?newsletter with their authorization. The content has been shared by the author for publication, with any modifications made solely for clarity and formatting. The views and opinions expressed are those of the author and do not reflect the official policies or positions of IBM or any other organization. This content is for?informational and educational purposes only?and should not be considered financial, legal, or professional advice. AI systems, particularly those leveraging large language models (LLMs), come with inherent risks, including biases, limitations in real-time adaptability, and ethical considerations. Organizations looking to deploy AI solutions should conduct thorough testing, adhere to governance frameworks, and ensure compliance with industry regulations. Some images in this article may be?AI-generated. All efforts have been made to ensure accuracy and proper attribution. By engaging with this content, readers acknowledge that the authors and publisher are not responsible for any decisions made based on the information provided.

?