DeepSeek explained: Everything you need to Know

In the world of AI, there has been a prevailing notion that developing leading-edge large language models requires significant technical and financial resources. That's one of the main reasons why the U.S. government pledged to support the $500 billion Stargate Project announced by President Donald Trump.

 

But Chinese AI development firm DeepSeek has disrupted that notion. On Jan. 20, 2025, DeepSeek released its R1 LLM at a fraction of the cost that other vendors incurred in their own developments. DeepSeek is also providing its R1 models under an open source license, enabling free use.

Within days of its release, the DeepSeek AI assistant -- a mobile app that provides a chatbot interface for DeepSeek-R1 -- hit the top of Apple's App Store chart, outranking OpenAI's ChatGPT mobile app. The meteoric rise of DeepSeek in terms of usage and popularity triggered a stock market sell-off on Jan. 27, 2025, as investors cast doubt on the value of large AI vendors based in the U.S., including Nvidia. Microsoft, Meta Platforms, Oracle, Broadcom and other tech giants also saw significant drops as investors reassessed AI valuations.

 

What is DeepSeek?

DeepSeek

DeepSeek is an AI development firm based in Hangzhou, China. The company was founded by Liang Wenfeng, a graduate of Zhejiang University, in May 2023. Wenfeng also co-founded High-Flyer, a China-based quantitative hedge fund that owns DeepSeek. Currently, DeepSeek operates as an independent AI research lab under the umbrella of High-Flyer. The full amount of funding and the valuation of DeepSeek have not been publicly disclosed.

 

DeepSeek focuses on developing open source LLMs. The company's first model was released in November 2023. The company has iterated multiple times on its core LLM and has built out several different variations. However, it wasn't until January 2025 after the release of its R1 reasoning model that the company became globally famous.

The company provides multiple services for its models, including a web interface, mobile application and API access.

OpenAI vs. DeepSeek

DeepSeek Vs OpenAI

DeepSeek represents the latest challenge to OpenAI, which established itself as an industry leader with the debut of ChatGPT in 2022. OpenAI has helped push the generative AI industry forward with its GPT family of models, as well as its o1 class of reasoning models.

 

While the two companies are both developing generative AI LLMs, they have different approaches.

	OpenAI	DeepSeek
Founding year	2015	2023
Headquarters	San Francisco, Calif.	Hangzhou, China
Development focus	Broad AI capabilities	Efficient, open source models
Key models	GPT-4o, o1	DeepSeek-V3, DeepSeek-R1
Specialized models	Dall-E (image generation), Whisper (speech recognition)	DeepSeek Coder (coding), Janus Pro (vision model)
API pricing (per million tokens)	o1: $15 (input), $60 (output)	DeepSeek-R1: $0.55 (input), $2.19 (output)
Open source policy	Limited	Mostly open source
Training approach	Supervised and instruction-based fine-tuning	Reinforcement learning
Development cost	Hundreds of millions of dollars for o1 (estimated)	Less than $6 million for DeepSeek-R1, according to the company

Training innovations in DeepSeek

DeepSeek uses a different approach to train its R1 models than what is used by OpenAI. The training involved less time, fewer AI accelerators and less cost to develop. DeepSeek's aim is to achieve artificial general intelligence, and the company's advancements in reasoning capabilities represent significant progress in AI development.

 

In a research paper, DeepSeek outlines the multiple innovations it developed as part of the R1 model, including the following:

• Reinforcement learning. DeepSeek used a large-scale reinforcement learning approach focused on reasoning tasks.
• Reward engineering. Researchers developed a rule-based reward system for the model that outperforms neural reward models that are more commonly used. Reward engineering is the process of designing the incentive system that guides an AI model's learning during training.
• Distillation. Using efficient knowledge transfer techniques, DeepSeek researchers successfully compressed capabilities into models as small as 1.5 billion parameters.
• Emergent behavior network. DeepSeek's emergent behavior innovation is the discovery that complex reasoning patterns can develop naturally through reinforcement learning without explicitly programming them.

 

DeepSeek large language models

China Deepseek

Since the company was created in 2023, DeepSeek has released a series of generative AI models. With each new generation, the company has worked to advance both the capabilities and performance of its models:

• DeepSeek Coder. Released in November 2023, this is the company's first open source model designed specifically for coding-related tasks.
• DeepSeek LLM. Released in December 2023, this is the first version of the company's general-purpose model.
• DeepSeek-V2. Released in May 2024, this is the second version of the company's LLM, focusing on strong performance and lower training costs.
• DeepSeek-Coder-V2. Released in July 2024, this is a 236 billion-parameter model offering a context window of 128,000 tokens, designed for complex coding challenges.
• DeepSeek-V3. Released in December 2024, DeepSeek-V3 uses a mixture-of-experts architecture, capable of handling a range of tasks. The model has 671 billion parameters with a context length of 128,000.
• DeepSeek-R1. Released in January 2025, this model is based on DeepSeek-V3 and is focused on advanced reasoning tasks directly competing with OpenAI's o1 model in performance, while maintaining a significantly lower cost structure. Like DeepSeek-V3, the model has 671 billion parameters with a context length of 128,000.
• Janus-Pro-7B. Released in January 2025, Janus-Pro-7B is a vision model that can understand and generate images.

 

Why it is raising alarms in the U.S

While there was much hype around the DeepSeek-R1 release, it has raised alarms in the U.S., triggering concerns and a stock market sell-off in tech stocks. On Monday, Jan. 27, 2025, the Nasdaq Composite dropped by 3.4% at market opening, with Nvidia declining by 17% and losing approximately $600 billion in market capitalization.

 

DeepSeek is raising alarms in the U.S. for several reasons, including the following:

• Cost disruption. DeepSeek claims to have developed its R1 model for less than $6 million. The low-cost development threatens the business model of U.S. tech companies that have invested billions in AI. DeepSeek is also cheaper for users than OpenAI.
• Technical achievement despite restrictions. The export of the highest-performance AI accelerator and GPU chips from the U.S. is restricted to China. Yet, despite that, DeepSeek has demonstrated that leading-edge AI development is possible without access to the most advanced U.S. technology.
• Business model threat. In contrast with OpenAI, which is proprietary technology, DeepSeek is open source and free, challenging the revenue model of U.S. companies charging monthly fees for AI services.
• Geopolitical concerns. Being based in China, DeepSeek challenges U.S. technological dominance in AI. Tech investor Marc Andreessen called it AI's "Sputnik moment," comparing it to the Soviet Union's space race breakthrough in the 1950s.

 

DeepSeek Bans

DeepSeek Ban

Countries and organizations around the world have already banned DeepSeek, citing ethics, privacy and security issues within the company. Because all user data is stored in China, the biggest concern is the potential for a data leak to the Chinese government. The LLM was also trained with a Chinese worldview -- a potential problem due to the country's authoritarian government.

Places where DeepSeek is banned include the following:

• Australian government agencies.
• India central government.
• Italy.
• NASA.
• South Korea industry ministry.
• Taiwan government agencies.
• Texas state government.
• U.S. Congress.
• U.S. Navy.
• U.S. Pentagon.

 

DeepSeek Cyberattack

DeepSeek's popularity has not gone unnoticed by cyberattackers.

On Jan. 27, 2025, DeepSeek reported large-scale malicious attacks on its services, forcing the company to temporarily limit new user registrations. The timing of the attack coincided with DeepSeek's AI assistant app overtaking ChatGPT as the top downloaded app on the Apple App Store.

Despite the attack, DeepSeek maintained service for existing users. The issue extended into Jan. 28, when the company reported it had identified the issue and deployed a fix.

DeepSeek has not specified the exact nature of the attack, though widespread speculation from public reports indicated it was some form of DDoS attack targeting its API and web chat platform.

 

DeepSeek data Exposed

Wiz Research -- a team within cloud security vendor Wiz Inc. -- published findings on Jan. 29, 2025, about a publicly accessible back-end database spilling sensitive information onto the web -- a "rookie" cybersecurity mistake. Information included DeepSeek chat history, back-end data, log streams, API keys and operational details. DeepSeek took the database offline shortly after being informed. It's unclear for how long the database was exposed.

 

 

 

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How to Run DeepSeek Locally on Your Machine: A Step-by-Step Guide

How to Run DeepSeek Locally

In the ever-evolving world of artificial intelligence and machine learning, running powerful AI models locally on your machine has become increasingly accessible. DeepSeek, a cutting-edge AI framework, is no exception. Whether you're a developer, data scientist, or AI enthusiast, running DeepSeek locally can provide you with unparalleled flexibility and control over your AI projects. In this blog, we'll walk you through the process of setting up and running DeepSeek on your local machine, ensuring you can harness its full potential.

 

Why Run DeepSeek Locally?

Before diving into the technical details, let's explore why running DeepSeek locally is beneficial:

1. Privacy and Security: By running DeepSeek locally, you ensure that your data remains on your machine, reducing the risk of data breaches.
2. Customization: Local execution allows you to tweak and customize the model to suit your specific needs.
3. Offline Access: Once set up, you can use DeepSeek without an internet connection, making it ideal for environments with limited connectivity.
4. Performance: Running the model locally can often result in faster processing times, especially if you have a powerful machine.

Prerequisites

 

Before we begin, ensure that your machine meets the following requirements:

1. Operating System: Windows, macOS, or Linux

2. Python: Version 3.7 or higher

3. GPU: Optional but recommended for faster processing (NVIDIA GPU with CUDA support)

4. RAM: At least 8GB (16GB or more recommended)

5. Storage: Sufficient space for the model and datasets (SSD recommended for faster access)

Step 1: Install Python and Required Libraries

 

First, ensure that Python is installed on your machine. You can download the latest version of Python from the  official Python website.

Once Python is installed, open your terminal or command prompt and install the necessary libraries using pip:

bash

pip install torch torchvision torchaudio
pip install transformers
pip install deepseek

These libraries include PyTorch, which is essential for running DeepSeek, and the transformers library, which provides pre-trained models and utilities for natural language processing.

 

Step 2: Download the DeepSeek Model

Next, you'll need to download the DeepSeek model. You can do this using the transformers library:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "deepseek/deepseek-llm"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

This code snippet downloads the DeepSeek model and its corresponding tokenizer, which are essential for processing text inputs.

 

Step 3: Set Up Your Environment

To ensure optimal performance, it's crucial to set up your environment correctly. If you have an NVIDIA GPU, make sure that CUDA and cuDNN are installed. You can verify this by running:

bash

nvidia-smi

If your GPU is recognized, you can enable GPU acceleration by moving the model to the GPU:

python

import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

Step 4: Run DeepSeek Locally

 

Now that everything is set up, you can run DeepSeek locally. Here's a simple example of how to generate text using the DeepSeek model:

python

input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_length=50)

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

 

This code takes an input text, processes it using the DeepSeek model, and generates a continuation of the text. The max_length parameter controls the length of the generated text.

Step 5: Optimize and Fine-Tune

 

Running DeepSeek locally allows you to fine-tune the model on your specific dataset. Fine-tuning can significantly improve the model's performance on your particular use case. Here's a basic example of how to fine-tune the model:

python

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir="./logs",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=your_train_dataset,
    eval_dataset=your_eval_dataset,
)

trainer.train()

Replace your_train_dataset and your_eval_dataset with your actual datasets. Fine-tuning can take some time, especially if you're working with a large dataset, but the results are often worth the effort.

 

Conclusion

Running DeepSeek locally on your machine is a powerful way to leverage AI for your projects. By following the steps outlined in this guide, you can set up, run, and even fine-tune the DeepSeek model to meet your specific needs. Whether you're developing AI applications, conducting research, or simply exploring the capabilities of AI, running DeepSeek locally offers a level of control and flexibility that cloud-based solutions simply can't match.

 

So, what are you waiting for? Dive into the world of DeepSeek and unlock the full potential of AI on your local machine today!

 

 

 

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Quantum Computing: The Future of High-Speed Data Processing

Quantum Computing 

What is Quantum Computing?

Quantum computing is an advanced technology that leverages the principles of quantum mechanics to process information at unprecedented speeds. Unlike classical computers that use bits (0s and 1s), quantum computers use qubits, which can exist in multiple states simultaneously due to superposition and entanglement. This revolutionary computing power has the potential to transform industries such as cybersecurity, artificial intelligence (AI), pharmaceuticals, and financial modeling.

 

How Does Quantum Computing Work?

Qubits, which can support parallel processing and complex problem solving beyond any possible classical computer, are what quantum computers work with. Main principles that enable the power of quantum computing are as follows: 

• Superposition - Qubits exist in a multiple state that enables quantum computers to process multiple calculations at a go.
• Entanglement - a phenomenon that connects qubits and allows the instantaneous correlation of data regardless of distance.
• Quantum Tunneling - Allows qubits to bypass traditional computational barriers, speeding up problem-solving processes.

 

Applications of Quantum Computing

Application of Quantum Computing 

Quantum computing is set to disrupt multiple sectors with its ability to solve problems that are currently beyond the capabilities of classical computers.

• Cybersecurity and Cryptography – Quantum computers can crack traditional encryption methods but also pave the way for quantum-resistant cryptographic algorithms to enhance data security.
• Artificial Intelligence (AI) and Machine Learning – Quantum computing accelerates AI training and optimization, leading to more efficient models for data analysis and automation.
• Pharmaceuticals and Drug Discovery – Simulating molecular structures for faster drug development and personalized medicine.
• Financial Modeling and Risk Analysis – Enhancing portfolio optimization, fraud detection, and market predictions through complex simulations.
• Climate Science and Materials Research – Improving climate models, discovering new materials, and optimizing renewable energy sources.

 

Leading Companies in Quantum Computing

Several tech giants and startups are pioneering quantum computing research and development:

• IBM – A leader in quantum computing with its IBM Quantum Experience and Qiskit framework.
• Google – Achieved quantum supremacy with its Sycamore processor.
• Microsoft – Developing topological qubits for scalable quantum computing.
• Intel – Advancing quantum hardware and quantum chips.
• D-Wave Systems – Specializing in quantum annealing technology for optimization problems.

The Future of Quantum Computing

Quantum computing is still in its early stages but is advancing rapidly. With ongoing breakthroughs in quantum hardware, software, and algorithms, the potential for real-world applications is expanding. Governments, enterprises, and researchers are heavily investing in this technology, anticipating revolutionary impacts across industries.

 

Final Thoughts

Quantum computing represents the next frontier in computational power, promising breakthroughs in AI, security, medicine, and more. As technology continues to evolve, quantum computers will become more accessible, reshaping the way we process and analyze data. Stay ahead of the curve and explore the limitless possibilities of quantum computing today!

Stay updated on the latest in quantum computing and be part of the future of technology!

 

 

 

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