What Is DeepSeek? The Reasoning Model That Shook the AI Industry

DeepSeek is an AI research lab and model family, developed by a Chinese AI company, that attracted enormous attention across the technology industry.

Its rise became significant for a specific combination of reasons: strong reasoning models, open-weight releases, solid performance on difficult technical tasks, and the ability to run certain models locally instead of only through a hosted API.

This article covers what DeepSeek actually is, how reasoning models differ from conventional language models, why DeepSeek became such a major story in AI, and how to run a DeepSeek model locally with Ollama.

Reasoning Models: Thinking Before Answering

Traditional language models generally generate responses by predicting the next tokens based on patterns learned during training.

Reasoning models introduced a different approach for difficult problems.

Models such as DeepSeek-R1 and OpenAI's reasoning models are designed to spend additional computation working through complex problems before producing a final response.

Instead of immediately producing an answer to a hard mathematical or programming problem, a reasoning model can break the problem into steps, evaluate intermediate results, identify inconsistencies, and revise its approach before reaching a final answer.

That extra computation is one reason reasoning models can take longer to respond.

Reasoning is not the model "thinking like a human." It's a computational process that lets the model spend more inference-time effort on harder problems.

Modern reasoning systems have become particularly useful for:

• Mathematics
• Programming
• Logic
• Complex analysis
• Multi-step problem solving
• Technical planning

Why Did DeepSeek Cause So Much Attention?

DeepSeek became a major story because it challenged assumptions about the cost and infrastructure required to build competitive AI systems.

The release of DeepSeek-R1 in early 2025 drew significant attention because the model demonstrated strong reasoning capabilities, and DeepSeek published technical details and released model weights that let developers and researchers experiment with the technology more openly than usual.

That created a much broader discussion around:

• AI training costs
• GPU demand
• Model efficiency
• Open-weight AI
• Inference optimization
• AI infrastructure
• The competitive landscape between major AI companies

The market reaction was significant too — NVIDIA and other semiconductor companies saw major volatility as investors reconsidered assumptions about how much compute would be required to scale advanced AI systems.

The deeper lesson wasn't that one model had "beaten" the entire AI industry. It was that architecture, training techniques, data efficiency, and inference optimization can dramatically shift the economics of AI development — sometimes more than raw compute spend does.

Running DeepSeek Locally With Ollama

One of the more interesting possibilities for developers is running compatible DeepSeek models locally.

Tools such as Ollama make it relatively straightforward to download and run supported open-weight models on your own machine:

ollama run deepseek-r1:8b

Once the model is downloaded, inference can happen locally without sending every prompt to a remote API.

This can be useful for:

• Privacy-sensitive development
• Offline experimentation
• Local AI assistants
• Prototyping
• Learning how LLM inference actually works
• Reducing API costs for certain workloads

"Local" doesn't automatically mean every piece of data is completely private. You should still understand what software you're running, where the model weights came from, and whether the surrounding application sends anything to external services.

What Do 1.5B, 7B, 8B, and 70B Mean?

Model names often include numbers such as 1.5B, 7B, 8B, 14B, 32B, or 70B.

The "B" generally represents billions of parameters — the learned numerical values inside a neural network that determine how it processes and generates information.

Larger models generally need more memory and compute:

Model size	Typical trade-off
Smaller models	Easier to run locally, generally faster
Medium-sized models	Stronger capabilities, higher hardware requirements
Large models	Potentially stronger, substantially higher memory and compute needs

RAM and VRAM requirements depend on model architecture, quantization, context length, and inference framework — parameter count alone doesn't determine whether a model will run on a given machine.

Quantization can significantly reduce memory requirements by representing model weights with fewer bits.

What Can You Ask a Local DeepSeek Model?

Once a compatible model is running locally, you can interact with it much like any other LLM:

• Explain this algorithm
• Debug this React component
• Solve this mathematical problem
• Design a PostgreSQL schema
• Explain this networking concept
• Review this API architecture

A smaller reasoning model can still be useful for programming and technical experimentation, though capability varies considerably across different versions and sizes.

For developers, the real advantage is being able to experiment with AI infrastructure directly, instead of treating an LLM as a black-box API you only reach through HTTP requests.

DeepSeek and Open-Weight AI

One of DeepSeek's most important contributions to the AI ecosystem has been expanding access to high-performance open-weight models.

Open-weight models let developers and researchers download model parameters and run supported versions themselves. That's different from a fully open-source system, where every component — training data, training code, infrastructure, and model weights — is openly available.

That distinction matters. When evaluating an AI model, look at:

• Model weights
• License
• Training information
• Architecture
• Benchmark results
• Hardware requirements
• Quantization options
• Deployment restrictions

That gives a more accurate picture of what "open" actually means for a given model, rather than assuming "open-weight" and "open-source" are interchangeable.

Frequently Asked Questions

Is DeepSeek free?

Some DeepSeek models are available as downloadable open-weight models that can be run locally. Local execution doesn't require paying an API provider, though you still need suitable hardware and storage. Cloud-hosted DeepSeek services and APIs may have their own pricing and usage limits.

What computer do I need?

It depends heavily on model size and quantization. Smaller models run on fairly modest hardware; larger models need substantially more RAM and/or VRAM. A practical approach is to start with a smaller model and get a feel for the trade-off between size, speed, memory usage, and capability before going bigger.

Are reasoning models better than traditional models?

Not for every task. Reasoning models are particularly useful for complex mathematical, programming, and multi-step reasoning problems, but simpler models can be faster and more efficient for straightforward tasks. The best model depends on the workload, not on which one scores highest on a benchmark in general.

Does running DeepSeek locally require the internet?

After downloading the model and required software, local inference can generally run without an internet connection. Downloading models, updating software, or connecting your application to external services will still require network access.

Conclusion

DeepSeek matters not simply because it's another AI model.

Its impact comes from what it demonstrated about the direction of AI: reasoning, efficient training, inference optimization, open-weight models, and local deployment are becoming increasingly accessible to individual developers, not just large labs.

For software engineers, that's an opportunity. You don't have to interact with AI only through a chatbot — you can download models, run inference locally, build applications around them, evaluate their performance, and integrate them into your own systems.

The next generation of AI engineers will need more than prompt-writing skills. They'll need to understand models, inference, APIs, GPUs, data pipelines, evaluation, deployment, and system architecture — and DeepSeek is a practical entry point into that.