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What Is DeepSeek?
Artificial intelligence (AI) is rapidly evolving, and with it comes a suite of tools that allow developers, researchers, and innovators to build smarter, more adaptive systems. One such emerging tool is DeepSeek: a powerful, open-source large language model (LLM) designed to rival the capabilities of major LLMs such as GPT-4 and LLaMA. But what exactly is DeepSeek, and why should you care?
In this chapter, we're going to dive into what DeepSeek is, how it fits into the broader AI landscape, and why it's generating interest across the tech industry. You'll gain an understanding of DeepSeek's unique features and how it compares to other models in terms of training data, efficiency, and performance benchmarks.
By the end of this chapter, you'll be equipped to understand the development of DeepSeek and key contributors to its success.
In this chapter, we're going to cover the following main topics:
- Introducing DeepSeek
- Understanding the technical breakthroughs of DeepSeek
- Impact on the global AI ecosystem
- Exploring the versions and evolution of DeepSeek
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Introducing DeepSeek
DeepSeek is an open-source language model that aims to democratize and make advanced AI accessible. The first version, DeepSeek-R1, appeared on 20 January 2025, just before the Chinese New Year. Instead of shipping only a closed binary, the team published the weights, training scripts, and inference code, so anyone can examine or rebuild the system.
Released under the MIT license, the model carries no usage fees or strict terms. Anyone may run it locally or adapt it for new tasks. This freedom drew developers, researchers, teachers, and small firms worldwide. They apply DeepSeek-R1 in support bots, classroom aids, lab studies, and writing tools.
Benchmarks show DeepSeek-R1, competing with OpenAI-o3 and Gemini-2.5-Pro. It handles math, code, many languages, and complex prompts. The results suggest strong models need not be closed and underscores China's growing role in frontier AI. The release also revived debates on open access and safety, and improving global research cooperation. On September 17, 2025, another milestone was reached as the DeepSeek-AI team published their research on the model DeepSeek-R1 in Nature and made it to the cover of that issue (https://www.nature.com/articles/s41586-025-09422-z).
From an architectural standpoint, DeepSeek leaned heavily on innovations in transformer-based models, while adding its own spin in later versions (explored in depth in the section, Versions and evolution of DeepSeek). But what made it truly stand out was its usability. DeepSeek could be deployed in a wide range of environments, from cloud servers to edge devices, and even laptops using lightweight versions.
Figure 1.1: Benchmark performance of DeepSeek-R1 (0528) (source: https://api-docs.deepseek.com/news/news250528)
Let's talk about the various factors that contributed to the sudden rise and popularity of DeepSeek:
- Open source architecture and training details: DeepSeek-R1 was released with a detailed research paper (https://arxiv.org/abs/2501.12948) outlining its architecture and training approach, benchmark scores across reasoning, math, and programming tasks (https://artificialanalysis.ai/providers/deepseek). This release was supported by full model weights, configuration files, and training scripts, along with six smaller distilled variants suited for local or low-resource environments (https://api-docs.deepseek.com/news/news250120) and immediate API availability (https://api-docs.deepseek.com/guides/reasoning_model) for developers wanting hosted access.
- Timing: Part of its popularity was due to timing, as global organizations, scientists, and developers began exploring the new release. Additionally, the MIT license provided complete freedom for commercial use - an increasingly rare trait among performant models. The release also sparked excitement because it was not a research-only artifact; it was practical. Developers were able to fine-tune it, deploy it in production environments, and integrate it into existing AI workflows. The combination of power and usability became an instant draw.
- Initial technical highlights: The most notable aspects of DeepSeek-R1 at launch included the following:
- Reasoning: It outperformed or matched leading models on key benchmarks involving mathematics, code, and logical reasoning.
- Efficiency: It provided performance close to GPT-4-level systems at significantly lower inference costs.
- Reinforcement-first training: Unlike conventional fine-tuning workflows that depend on supervised human-annotated data, DeepSeek skipped straight to reinforcement learning from human feedback (RLHF) or similar paradigms - though with minimal human labeling. The change sped up reasoning scores, lowered human-labeling costs, and allowed the model to tackle diverse tasks, such as math problems, zero-shot code, and so on, without needing narrow task-specific instructions.
- Custom architecture: While based on the transformer framework, DeepSeek-R1 incorporated innovations optimized for training stability and long-context understanding.
These elements together enabled the model to punch well above its weight, especially in multi-step reasoning and problem solving.
- Real-world readiness: DeepSeek demonstrated real-world readiness from the outset, standing apart from many state-of-the-art models that excel in benchmarks but struggle in deployment. Unlike others that require extensive setup or closed infrastructure, DeepSeek was immediately usable in practical settings. It offered production-ready access via its API, local deployment with open weights and inference code, and customization through LoRA fine-tuning or prompt engineering. Integration with enterprise platforms such as Trae and Windsurf further streamlined orchestration. These capabilities, rarely combined so seamlessly in other models at launch, underscored DeepSeek's commitment to practical utility beyond academic performance.
- Community interest: DeepSeek-R1's release sparked intense community activity. GitHub quickly overflowed with plug-ins, adapters, and fine-tuned spin-offs, while thousands of Hugging Face forks powered tools for contract review, tutoring, research aid, summarization, and coding support. Online forums shared benchmarks and hardware guides, and universities adopted the model for courses and lab projects. Forums such as Reddit, Zhihu, and Stack Overflow buzzed with shared experiments, performance tests, and guides for fine-tuning DeepSeek on local hardware. The accessibility of the model turned casual enthusiasts into researchers and developers into entrepreneurs. Today, DeepSeek also fuels educational initiatives. Several MOOCs and university labs have begun teaching LLM theory and experimentation using DeepSeek as the base model due to its openness and clarity.
- Philosophical vision: DeepSeek's vision aligns with a broader movement to build AI not as a gatekept asset, but as a shared global resource. Much like how Linux reshaped the software industry, DeepSeek aims to reshape AI development by putting tools in the hands of anyone curious or capable enough to use them. Its strategy is not just to compete with OpenAI or Google but to focus on accessibility and collaborative innovation.
DeepSeek-R1's success is attributed to three factors: RLHF training, open source commitment, and its competitive performance across benchmarks.
Together, these elements laid the groundwork for DeepSeek not just as a model, but as an ecosystem. The remainder of this chapter will explore the reception of DeepSeek-R1 and the motivations behind its open philosophy, what technical breakthroughs powered it, and how it is evolving into a full-scale ecosystem.
Understanding the technical breakthroughs of DeepSeek
What truly sets DeepSeek-R1 apart are the technical innovations embedded in its architecture and training process. These innovations enabled it to outperform many contemporary models and helped redefine how future LLMs might be built.
The training process
Before we begin with DeepSeek's training process, let's first take a look at the development process of leading LLMs, which usually consists of the following:
- Pretraining on massive corpora using self-supervised learning: In this stage, models are exposed to large-scale, diverse datasets such as books, websites, and code. The goal is to learn general...
