How to Build an LLM Like DeepSeek?

 

How to Build an LLM Like DeepSeek?

China has significantly impacted the global AI landscape by launching DeepSeek, an AI language model that competes with major players like ChatGPT, Claude, Llama, and Gemini. Even more impressive is that DeepSeek built this powerful model at a fraction of the required cost and resources.

Founded by Liang Wenfeng in December 2023, DeepSeek introduced its free AI chatbot in January 2025, powered by its exclusive language model. 

Despite utilizing just over 2,000 Nvidia H800 chips, DeepSeek's model outperformed leading models in multiple tests, making a compelling case for how smaller, more resource-efficient models can compete at the highest levels.

In this article, we'll explore how DeepSeek created a cutting-edge LLM at a fraction of the cost and how other AI startups can take similar steps to build their models. We'll also discuss how partnering with a trusted AI development company like Cubix can help.

What is DeepSeek and Why Is It Making Waves?

DeepSeek is a rapidly emerging player in the AI space, revolutionizing language model architecture with innovations that make high-level AI capabilities accessible at lower costs. One of its primary differentiators is using a Mixture-of-Experts (MoE) design, which activates only a subset of parameters for each input, reducing redundancy and improving efficiency.

DeepSeek also incorporates reinforcement learning to enhance the model's reasoning capabilities. This allows it to learn through trial and error rather than relying on large, labelled datasets. This combination of specialized architecture and innovative training techniques has enabled DeepSeek to achieve benchmark performances comparable to models like GPT-4, with significantly lower hardware and resource requirements.

How DeepSeek Works

The DeepSeek Mixture-of-Experts (MoE) Design

The core innovation behind DeepSeek is its MoE model. This approach divides the model into specialized sub-components, or "experts," and activates only the most relevant ones for each input. By using this method, DeepSeek reduces the overall computational burden while still achieving top-tier performance.

1. Segmentation for Specialization: DeepSeek splits large modules into smaller, specialized "experts" rather than relying on a few generalized ones. This allows for more focused learning in narrower domains.
2. Isolating Shared Knowledge: Some general knowledge, like grammar rules or common facts, is isolated into "shared experts" that are always active. This prevents specialists from duplicating common knowledge, freeing their capacity for more specialized tasks.
3. Dynamic Load Balancing: DeepSeek ensures that the workload is evenly distributed among the experts, avoiding bottlenecks and improving efficiency.

Reinforcement Learning for Reasoning

DeepSeek incorporates reinforcement learning (RL) to enhance the model's reasoning ability and solve complex tasks. Unlike traditional supervised learning, which requires massive labelled datasets, RL enables the model to learn through trial and error, receive feedback, and adjust its strategies to improve performance.

DeepSeek's RL system is structured in stages:

• Stage 1: Reasoning from Scratch: The model begins by learning basic reasoning tasks through reinforcement learning.
• Stage 2: Human-in-the-Loop: The model is then guided by a small set of human-provided examples to help it understand human communication patterns.
• Stage 3: Simulation to Reality: The model is fine-tuned using large datasets and real-world examples to improve its practical applications.

How DeepSeek Achieved Benchmark-Topping Results on a Budget

Despite using only about 2,000 GPUs, DeepSeek has demonstrated performance that rivals models with billions of parameters. Its ability to solve advanced mathematical problems and perform high-level coding tasks showcases how practical and efficient training and specialized architectures can be, even with limited hardware.

DeepSeek's model proves that LLMs don't necessarily need massive computational resources or substantial data volumes to achieve competitive performance. 

DeepSeek has broken the conventional assumption that AI development must always involve scaling up hardware resources by focusing on task-specific specialization and efficient training.

How to Build an LLM Like DeepSeek

DeepSeek's success shows that creating a competitive AI model doesn't always require vast data or computational power. Here's how to build an LLM like DeepSeek:

1. Embrace the Mixture-of-Experts (MoE) Design

At the heart of DeepSeek's model is the MoE design, which activates only a subset of parameters for each input. This reduces redundancy and makes computation more efficient.

Key Architectural Considerations:

• Expert Count and Specialization: Choose the number and types of experts based on the tasks and computational budget.
• Static vs. Dynamic Experts: Use a mix of static and dynamic experts, with dynamic ones being activated based on context.
• Router Mechanism: Implement a router system to decide which experts to activate through simple cycles or more complex predictions.
• Training Across Experts: Ensure that each expert focuses on specific tasks and minimises redundancy.

2. Use Reinforcement Learning for Reasoning

DeepSeek enhances its model's reasoning capabilities through reinforcement learning, allowing it to learn by interacting with its environment and receiving feedback.

Key Training Principles:

• Self-Play Environments: Create scenarios where the model learns by solving puzzles and answering questions.
• Automated Scoring: Implement automated systems to assess the quality of the model's output and provide feedback.
• Staggered Training: Gradually increase the complexity of tasks to allow the model to master foundational skills before moving on to more advanced ones.

The Outcome: Expertise + Efficiency

By combining efficient architecture with reinforcement learning, DeepSeek has achieved remarkable results with a relatively small model. This approach demonstrates that creating high-performing, resource-efficient AI systems is possible without relying on massive datasets or vast computational power.

DeepSeek's success proves that AI models can be designed to maximize efficiency, enabling startups and smaller organizations to create impactful AI solutions with limited resources.

Partnering with Cubix for Your LLM Development

DeepSeek's breakthrough shows that smaller companies can significantly impact the landscape. If you want to build your own successful LLM, partnering with an experienced AI development company like Cubix can help accelerate your AI initiatives. Cubix has a track record of building efficient, high-performance AI models for startups and businesses worldwide.

Frequently Asked Questions

1. How much does building an AI chatbot using DeepSeek R1 cost? Developing an AI chatbot with DeepSeek R1's capabilities could cost between $50,000 and $200,000, depending on the features and complexity of the integration.

2. How much does developing an AI model like DeepSeek R1 cost? Depending on the complexity, scale, and team size, the cost could range from $500,000 to $2,000,000+.

3. Can AI startups build an AI chatbot like DeepSeek or ChatGPT? Yes, thanks to innovations in model architecture and training techniques, AI startups can now create competitive models similar to DeepSeek or ChatGPT. With careful planning and the proper infrastructure, startups can develop high-performing chatbots on a budget.

4. What is the NVIDIA H800? The NVIDIA H800 is a powerful accelerator chip designed to enhance AI computing performance while lowering costs. It powers platforms like DeepSeek, making advanced AI development more affordable.