An expert in any subject spends many years learning, understanding, and accumulating knowledge. This knowledge can then be transmitted to a student through a well-structured course in the span of just a semester. A good student will learn the core concepts and, after finishing the course, will be able to give a well-informed response to a related question. The student may not learn every single detail the expert has learned, but they are functionally capable of using the knowledge gathered by the expert.

This analogy perfectly describes the distillation method used by Deepseek to optimize AI training.

It replaces the massive, expensive models by training a smaller, more efficient model. This smaller model, often called the “student”, is trained using knowledge extracted from a larger, more powerful “teacher” model. By doing this, Deepseek slashes the computational cost significantly while still maintaining a high level of performance.

The Origins of Model Distillation

The knowledge distillation technique was introduced and demonstrated by researchers Geoffrey Hinton, Oriol Vinyals, and Jeff Dean in their 2015 paper, Distilling the Knowledge in a Neural Network. This research demonstrated that a large, complex model could transfer working knowledge to a smaller, more efficient model. This could be done without sacrificing much of the performance.

Since then, various research teams have worked on refining the process of distillation. They have introduced advances such as task-specific distillation, multi-stage distillation, and temperature scaling. Many companies have worked and experimented with distillation as a technique, but Deepseek is the first to effectively take the technique to scale, deploying it in their large language models.

The Power of Model Distillation

AI training the traditional way requires huge computational power and storage. Such a resource-intensive approach makes it very expensive to run. Deepseek’s approach leverages distillation, where a powerful, pre-trained teacher model generates outputs and guides a smaller student model to replicate its behaviour. Over time, the student model learns to predict similar outputs with fewer parameters, leading to:

• Lower operational costs – Running smaller models requires less processing power and memory.
• Faster inference times – A leaner model can generate responses more quickly.
• Energy efficiency – Reducing computational demand decreases environmental impact.
• Accessibility – More companies can deploy high-performing AI without requiring massive infrastructure.

Optimizing Training with 8-bit Architecture

Beyond distillation, Deepseek further reduced costs by implementing an 8-bit architecture for model training. Traditional AI models usually rely on 16-bit or 32-bit floating point precision, which demands a lot of computational power and is hence very expensive. However, by quantizing models to 8-bit precision, Deepseek was able to:

• Cut memory usage in half, allowing more efficient use of hardware resources.
• Reduce power consumption, making AI training more sustainable.
• Speed up matrix computations, improving overall training efficiency.
• Run models on cheaper hardware, enabling cost-effective scaling.

What Does Running on 8-bit Architecture Mean?

This refers to the use of a lower-bit precision method that use 8-bit integers instead of 32-bit floating points when doing deep learning computations. This is a more efficient replacement of the standard 32-bit floating point (FP32). FP32 takes a lot of computational power and memory to run. It is highly precise but costly to run. That is why researchers developed more efficient methods, such as 16-bit (FP16) and 8-bit (INT8) quantization.

When an AI model is trained or executed using 8-bit precision, it means:

• The numerical values representing weights and activations are stored in 8-bit integers (INT8) instead of 32-bit floats, reducing the memory footprint significantly.
• Matrix multiplications and tensor operations—the backbone of deep learning—become faster because lower-bit computations require less processing power.
• Less energy is consumed, making large-scale AI training and inference more cost-effective and environmentally friendly.

This trade-off results in a slight loss of numerical precision, but modern quantization techniques ensure that this does not significantly impact the model’s performance. Deepseek has leveraged these optimizations to create a model that is both affordable and highly capable.

How Deepseek Offers High-Quality AI for Free

One of the most remarkable aspects of Deepseek’s approach is its ability to provide ChatGPT-like performance at no cost. This is made possible through a combination of:

• Efficient model training – Deepseek trains models using a fraction of the resources required by traditional LLMs, thanks to the use of distillation and 8-bit quantization.
• Smaller, optimized models – Large-scale models like ChatGPT require massive computational power to run. In comparison, Deepseek’s distilled models maintain nearly the same level of quality while being significantly more efficient.
• Leveraging open-source advancements – Deepseek stands on the shoulders of giants, taking advantage of existing research and tools. They work on refining what already exists instead of creating their own from scratch.
• Cloud and edge deployment strategies – Deepseek’s models can run efficiently on consumer hardware, making AI more accessible to everyone. Not requiring costly cloud-based computing gives it a significant upper hand.

By implementing these strategies, Deepseek has managed to provide a highly capable AI assistant without the need for expensive subscriptions or infrastructure, democratizing access to AI technology for startups, developers, and businesses alike.

Why This Matters for Startups

For startup founders building AI-powered products, cost efficiency is crucial. Training and running large language models can be prohibitively expensive. Deepseek’s distillation method, combined with 8-bit quantization, presents a practical alternative: offering the intelligence of a powerful model at a fraction of the cost.

This is a game-changer for product development, allowing startups to:

• Build AI-driven features without burning capital on GPU-heavy infrastructure.
• Deploy models that work efficiently on mobile devices and edge computing.
• Iterate faster by training smaller models that are easier to refine and improve.

The Future of AI Training

Deepseek’s approach highlights a major shift in AI— leaching behind brute-force training of massive models and leaning into smart, optimized learning techniques. As AI continues to evolve, more companies will adopt similar efficiency-driven strategies to balance cost, speed, and performance.

For startups, the lesson is clear: The future of AI isn’t in the biggest models, it is about training and deploying them efficiently.

At Capital Compute, we help founders navigate these emerging technologies, leveraging cutting-edge AI techniques while keeping development lean and scalable. If you’re building a startup product and want to integrate AI efficiently, let’s talk!