How to Build an LLM Like DeepSeek?

China has made quite a sudden impact in the global AI ecosystem by launching DeepSeek, their very own LLM that rivals the likes of ChatGPT, Claude, Llama, and Gemini. The best part? They built it for a mere fraction of the cost and resources!

DeepSeek, a company founded in December 2023 by Chinese technologist Liang Wenfeng, launched its free AI chatbot on 10th January 2025. It was based on their own exclusive LLM model. 

Now, here’s the catch!

Their model, especially the DeepSeek R1, was able to compete with ChatGPT and outperform big players like Claude, Gemini, and Llama across various tests.

Read More: Is Google’s New Gemini AI Better than ChatGPT?

Moreover, they achieved this incredible feat with just over 2000 Nvidia H800 chips. This shows just how cost-effective, resource-efficient, and sustainable AI development can be. 

In this blog, we’ll discuss how DeepSeek built an LLM model that’s just as good as ChatGPT for a fraction of the cost. We’ll also explore the steps AI startups can take to build such models on their own and how partnering with a trusted AI development company like Cubix can help.

What is DeepSeek and What’s the Buzz About?

DeepSeek is an emerging leader in artificial intelligence pushing boundaries with its innovative language models. This Chinese startup combines smart architecture and training strategies to achieve remarkable benchmarks at fractional costs compared to titans like OpenAI.

Read More: OpenAI vs. DeepMind – Key Difference Explained

At its core, DeepSeek owes its efficiency to a Mixture-of-Experts (MoE) design where only a subset of parameters are activated per input. This allows selective specialization, reducing redundancy plaguing complex models. Architectural innovations like segmented experts and shared modules isolate distinct skills, preventing overlapping knowledge.

However, hardware optimizations alone cannot power reasoning abilities. Here DeepSeek uses reinforcement learning, making models learn via trial-and-error interactions without huge labeled datasets. The synthesis of specialized MoE and reinforcement training allows DeepSeek to extract more performance per parameter.

Benchmark-Topping Results on a Budget

Despite utilizing only ~2000 GPUs, DeepSeek models have demonstrated parity with the most advanced AI, such as GPT-4. For instance, DeepSeek-R1 solves advanced mathematical reasoning tasks better than industry leaders, while DeepSeekMoE shows top-tier coding capabilities.

Paving the Road to Accessible and Sustainable AI

By challenging common assumptions that LLMs like GPT can only be built by investing billions in scalability, DeepSeek created a low-cost LLM that ended up performing just as well (or even better in some cases). 

The fact that it was able to match the performance of ChatGPT, Claude, and Gemini with a fraction of the hardware promotes sustainability, aligning with responsible AI efforts. This has also widened access and lowered barriers to access AI for companies across different industries.

DeepSeek’s breakthrough may shape an AI landscape with a greater diversity of solutions meeting specialized demands.

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How Does DeepSeek Work?

Before learning how to build an LLM like DeepSeek, let’s understand how this breakthrough AI model works:

The DeepSeek MOE

Mixture-of-Experts (MOE) is the key ingredient empowering DeepSeek models to match leading AI’s abilities on just a fraction of resources. MOE refers to structuring models with specialized sub-components, unlike complex architectures where every parameter gets activated regardless of relevance.

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DeepSeek enhances existing MOE approaches through architectural optimizations enabling enhanced efficiency and task-focused specialization.

Segmenting for Specialization

DeepSeek begins by dividing standard large modules into finer-grained “experts” rather than having a few large generalized ones. For instance, 16 broad experts become 64 focused specialized neural networks.

This granularity promotes experts concentrating efforts on narrow domains rather than spreading across tasks. Expert combinations also increase exponentially, allowing flexible activation targeting specific needs.

Isolating Shared Knowledge

DeepSeek further isolates universally relevant knowledge like grammar rules or common sense facts into “shared experts” that always remain activated. This avoids wasting specialist expert capacity on redundant generalized processing.

The segregation leaves main experts to build task-specific skills like mathematical logic solely, without handling both specialized and common knowledge.

Balancing Workloads Dynamically

To prevent overburdening particular experts, DeepSeek incorporates load-balancing techniques across training. This maintains a balance where all experts and hardware share work evenly, avoiding bottlenecks.

Together, the segmented and isolated experts minimize overlap while reducing computations by limiting unnecessary parameter activation. The outcome is specialized, efficient language architectures.

Reinforcement Learning

While the MOE enables efficiency, DeepSeek uses reinforcement learning (RL) to impart reasoning capabilities with minimal traditional supervision.

RL refers to goal-oriented trial-and-error learning centered around dynamic feedback. Here, models develop skills by attempting tasks, getting scores for success, and iteratively strategizing to increase rewards.

Learning Complex Reasoning

Reinforcement learning curbs data hunger plaguing supervised approaches. Instead of huge labeled datasets, the model acquires skills via practice interactions. DeepSeek combines rule-based scoring with tenacious attempts focusing on precise reasoning objectives.

Over cycles spanning codes, puzzles, questions, and more, DeepSeek assimilates specialized cognitive capabilities. The system latches onto tactics incrementally improving outcomes devoid of human guidance.

Strategic Staggered Reinforcement

DeepSeek productively channels RL’s exploration by structuring training across multiple stages with increasing complexity:

• Stage 1: Reasoning from Scratch: Foundational models like DeepSeek-R1-Zero skip supervision completely, building baseline reasoning solely via RL rewards on explanatory outputs for math, logic, etc.
• Stage 2: Human-in-the-Loop Guidance: Subsequent iterations like DeepSeek-R1 initialize training with a small volume of human examples. This “cold-start” data primes the model for comprehension and communication conventions when producing reasoned explanations.
• Stage 3: Simulation to Reality: Final fine-tuning comes through supervised practice on large datasets. This connects strategic reasoning with practical applications spanning text, code, and more.

Together these consecutive RL phases drive the mastery of reasoning abilities beyond surface pattern recognition to deeper analytical intelligence.

The Outcome: Expertise + Efficiency

DeepSeek’s meshing of architectural and training innovations results in remarkable benchmarks at fractional training costs. By specializing parameters and focusing computation per input via MOE, it averts complex models’ redundancy.

Meanwhile, reinforcement training unlocks reasoning prowess without proportional data needs. Through attempts alone, models learn to compose solutions and explanations across quantitative analyses.

The synthesis manifests in small DeepSeek systems exhibiting excellence in mathematical reasoning, programming, summarization, question answering, and more. Such replication of niche skills economically paves the path for accessible and sustainable AI.

Rather than stemming purely through scale, DeepSeek’s cost-effectiveness and performance spotlight how compositional design multipliers improve efficiency to unlock new value. Its blueprint for affordable excellence signals a shift toward democratized AI.

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How to Build an LLM Like DeepSeek?

The pursuit of bigger and better AI often concentrates computing into massive complex models with billions of parameters. However, DeepSeek showed how less can equal more when innovating architecture and training. 

Let’s break down the key pillars behind this AI model’s success and how to build an LLM like DeepSeek:

Mixture of Experts for Specialized Efficiency

At the foundation, DeepSeek embraces a Mixture-of-Experts (MoE) design where only a subset of parameters activate per input. This selective activation allows computational focus, sidestepping complex models’ redundancy.

MoE refers to organizing models into specialized modules. For instance, rather than a monolith, the system comprises smaller expert neural networks. A router then decides which experts to invoke per input.

Primary Architectural Decisions

Constructing an efficient MoE model involves core considerations:

• Expert Count and Sizing: Determine the number and variety of experts based on computational budget and tasks. Make sure you’re balancing granularity and specialization.
• Static vs. Dynamic Experts: Static experts stay fixed while dynamic ones conditionally activate based on context. We recommend blending both for efficiency.
• Router Mechanism: Simple routers sequentially cycle experts. Advanced routers weigh context to selectively activate. Complex routers use predictions.
• Specialized Training: It’s important to vary data across experts. Also, reinforce task-specific skills per expert while measuring redundancy.

Reinforcement Learning for Reasoning

While MoE enables computational resource effectiveness, solely supervising models on static datasets restricts reasoning skills. DeepSeek intensifies intelligence through reinforcement learning.

In RL, models learn via attempts, scoring feedback, and iteration without prescribed datasets. By rewarding explanatory reasoning across math, logic, code, and more, DeepSeek imbues analytical prowess.

Key Training Principles

Effectively channeling RL involves:

• Self-Play Environments: Curate puzzles, questions, and challenges spanning reasoning modalities.
• Automated Scoring: Program environments to dynamically score output quality without manual labeling.
• Staggered Curricula: Gradually grow complexity across segments. Master foundations before advancing challenges.
• Multi-Modality Reward: Incentivize reasoning, explanations, and formatting with composite rewards.

The Result: Efficient Architecture + Effective Training

Unifying lean MoE design with reinforcement honing concentrates computational power into specialized reasoning skills. Avoiding dense model extravagance, DeepSeek achieved performance only expected from multi-billion dollar LLM models. 

This shows how models can learn on their own rather than relying merely on data and scale. The principles above also highlight the importance of efficient, unconventional training and learning approaches for low-cost, high-performance, and sustainable AI. 

Developing a low-cost LLM model with minimal hardware muscle is highly possible with unique training and learning approaches.

Read More: How to Build AI Agents – A Comprehensive Guide

Build the Next Successful LLM With Cubix

DeepSeek’s recent breakthrough has surely opened doors for smaller AI startups to make an impact and drive more cost-effective, sustainable AI development, deployment, and adoption approaches.

If you’re a business owner looking to create the next successful LLM, you can always partner with Cubix.

We’re a trusted AI development company, trusted by AI startups and SMBs worldwide. Our teams build AI models that balance efficiency with performance.

Read More: Best Open Source Generative AI Models

Contact our representatives and we’ll see how we can help you accelerate your AI initiatives.

Frequently Asked Questions

How much does it cost to build an AI chatbot using DeepSeek R1?

In order to develop an AI chatbot using DeepSeek R1 capabilities, you can expect to spend somewhere between $50,000 to $200,000 including infrastructure and engineering expenses. The exact pricing depends on features and integration complexity.

How much does it cost to develop an AI model like DeepSeek R1?

The overall cost to build an AI model like DeepSeek R1 would likely fall between $500,000 to $2,000,000+ based on model complexity, scale, data, and team size. This cost factors in conceptualization, experimentation, and talent costs.

Is it possible for AI startups to build an AI chatbot like DeepSeek or ChatGPT?

DeepSeek’s recent breakthrough in the AI landscape has given AI startups worldwide a fighting chance. They now can create an impact with limited resources, budgets, and teams.

So, with careful planning around efficient model architecture and training techniques combined with cloud infrastructure, AI startups can create specialized and low-cost chatbots that can compete with the performance of enterprise-grade models like GPT, Llama, Claude, and Gemini.

What is the NVIDIA H800?

The NVIDIA H800 is an advanced accelerator chip designed to speed up AI computing and lower costs. Its high-performance processing powers platforms like DeepSeek cost-effectively.