DeepSeek: In-Depth Overview of the Rising AI Model Family

DeepSeek is rapidly gaining attention in the artificial intelligence landscape. It’s not just a single AI model, but a family of models, a chatbot powered by them, and the company behind it all. This post will delve into the key aspects of DeepSeek, from its origins and capabilities to its performance, availability, and even some ethical considerations.

DeepSeek Overview: Origins, Foundation, and Core Features

• General Information:
  • Definition: DeepSeek encompasses a family of advanced AI models, an interactive chatbot leveraging these models, and the company responsible for their creation.
  • Origin: Hailing from Hangzhou, Zhejiang, DeepSeek is a Chinese company, specifically Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd. It is owned and financially backed by High-Flyer, a Chinese hedge fund.
  • Founder & CEO: DeepSeek was established in July 2023 by Liang Wenfeng, who is also the co-founder and CEO of High-Flyer. He holds the CEO position for both entities.
  • Cost Efficiency: Remarkably, DeepSeek’s development was achieved with significantly less computational resources compared to industry giants. Its engineers utilized approximately $6 million in raw computing power, a fraction (roughly one-tenth) of the estimated cost for Meta’s latest AI development.
  • Date Founded: DeepSeek was officially founded in July 2023.
• Capabilities & Performance: DeepSeek is designed to excel in specific areas, showcasing notable strengths:
  • Technical and Mathematical Prowess: DeepSeek is particularly adept at technical tasks and mathematical computations, demonstrating proficiency in solving challenging math problems.
  • Efficient Architecture: DeepSeek employs an open-source AI model architecture that utilizes a Mixture-of-Experts (MoE) approach. This design makes it more efficient and cost-effective compared to models like ChatGPT.
  • Inference-Time Compute Optimization: The models utilize inference-time compute, which strategically breaks down complex queries into smaller, more manageable tasks, enhancing processing efficiency.
  • DeepThink Mode: A notable feature, DeepSeek’s DeepThink mode, provides transparency by showing its thought process when tackling complex problems, especially in mathematics.
  • Coding Assistant Excellence: DeepSeek V3 stands out as a top-tier AI coding assistant. Extensive hands-on coding experience has led developers to confidently declare DeepSeek V3 as their “top-choice LLM (Large Language Model)” for coding tasks.
  • Benchmark Performance: DeepSeek consistently stands out with its high performance in benchmarks, indicating strong capabilities across various AI evaluations.
  • Reasoning-Focused Reinforcement Learning: DeepSeek leverages a large-scale reinforcement learning approach specifically focused on reasoning tasks, contributing to its advanced problem-solving abilities.
• Availability & Access: DeepSeek aims for broad accessibility:
  • Global Accessibility: DeepSeek is making a global impact due to its affordability and strong performance.
  • Mobile App: An advanced AI chatbot app offering interactive conversations is readily available for free on both the Google Play Store and Apple App Store, ensuring easy access for a wide range of users.
  • API Access via OpenRouter: Developers can access the DeepSeek API for free through the OpenRouter platform.
  • Local Model Execution (DeepSeek-R1): Running DeepSeek-R1 locally provides users with complete control over model execution and removes reliance on external servers, offering enhanced privacy and customization.
  • User-Friendly Interface: The DeepSeek app is praised for being “incredibly intuitive” and “user-friendly”, making it easy to use even for individuals who are not tech-savvy.
• Open Source Nature: Openness is a core principle for DeepSeek:
  • Open-Source Model: DeepSeek is fundamentally an open-source AI model, promoting transparency and community contribution.
  • Commitment to Open Source: DeepSeek has reinforced its dedication to open source by announcing plans to make its models’ code publicly available.
  • R1 Reasoning Model Impact: The release of DeepSeek’s open-source R1 reasoning model significantly impacted the AI industry. It rivaled Western systems in performance while being developed at a lower cost, demonstrating the potential of efficient, open-source AI.
  • DeepSeek License: The model is made source-available under the DeepSeek License.
• Comparison to ChatGPT: DeepSeek and ChatGPT are often compared, and here’s a breakdown:
  • Efficiency and Cost-Effectiveness: DeepSeek’s architecture, particularly the Mixture-of-Experts approach, makes it more efficient and cost-effective than ChatGPT.
  • Task-Specific Strengths: While DeepSeek excels in technical queries, ChatGPT is considered superior in creative and conversational tasks.
  • Free and Powerful App: The DeepSeek app is free and utilizes a model that is demonstrably better than the free version of ChatGPT, offering more advanced capabilities without cost.
  • Speed for Technical Tasks: DeepSeek typically performs better for rapid code generation and technical tasks, exhibiting faster response times for structured queries.
• DeepSeek V3: Advanced Chat Capabilities:
  • High-Quality Chat Responses: DeepSeek V3 provides access to high-quality AI-driven chat responses, making it suitable for diverse applications.
  • Versatile Applications: DeepSeek V3 is well-suited for applications such as AI chatbots, automation systems, and content generation.
  • Training Cost Efficiency: The entire training process for Deepseek V3 cost approximately $5.5 million, highlighting its cost-effective development.
  • Top-Choice LLM for Coding: (Repeating for emphasis) After extensive coding projects, DeepSeek V3 is considered a “top-choice LLM (Large Language Model)” for coding.
  • Model Size Details: The total size of DeepSeek-V3 models on HuggingFace is 685B, including 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.
• Limitations of DeepSeek: While powerful, DeepSeek has limitations:
  • Inference Hardware Requirements: For basic inference tasks, a system with 16GB-32GB RAM is sufficient.
  • Training Hardware Requirements: However, model training demands significantly more resources, requiring a minimum of 32GB-128GB RAM along with high-end GPUs.
  • Geopolitical and Security Concerns: DeepSeek has been banned on all federal government systems and devices in Australia due to security and privacy concerns.
  • Potential Bias and Censorship: As a product of a Chinese company, concerns exist regarding potential CCP-mandated bias and censorship in its responses.
• DeepSeek-R1: Reasoning Powerhouse:
  • Multi-Stage Training: DeepSeek R1 achieves its advanced reasoning capabilities through a unique multi-stage training process.
  • Performance vs. OpenAI-o1: DeepSeek R1 offers performance comparable to OpenAI’s o1 model but at a more economical price point.
• DeepSeek Coder: Specialized for Code Generation:
  • Model Series: DeepSeek Coder is a collection of 8 distinct models, comprised of 4 pretrained (Base) models and 4 instruction-finetuned (Instruct) models.
  • Context Length: All models within the DeepSeek Coder series feature a 16K context length, enabling them to handle larger code segments and more complex coding tasks.
• Regarding Grok-3: While not explicitly detailed in the outline in relation to DeepSeek, it’s noted that Grok-3 is “touted as the “world’s smartest AI,””. This may be relevant for contextualizing the competitive AI landscape in which DeepSeek operates.

DeepSeek Math Specifics: Focused Mathematical Capabilities

• Core Competency in Math: Reinforcing its strengths: DeepSeek excels in technical tasks and mathematical computations, demonstrating its ability to solve challenging math problems.
• DeepSeekMath Model Variants:
  • DeepSeekMath-Instruct 7B: This model is a mathematically instructed tuning model derived from DeepSeekMath-Base 7B.
  • DeepSeekMath-RL 7B: Built upon DeepSeekMath-Instruct 7B, this model is further trained using Group Relative Policy Optimization (GRPO) algorithm.
• Mathematical Reasoning Focus: DeepSeek’s mathematical models are designed to test both computational ability and mathematical reasoning.

Benchmarks and Metrics: Evaluating Mathematical Proficiency

• Math 500 Benchmark: A Comprehensive Evaluation:
  • Benchmark Description: The Math 500 Benchmark is a comprehensive mathematics benchmark encompassing 500 problems across various mathematical domains, including algebra, calculus, probability, and more.
  • Assessment Focus: This benchmark specifically tests both computational ability and mathematical reasoning skills of AI models.
  • Score Interpretation: Higher scores on the Math 500 Benchmark directly correlate to stronger mathematical problem-solving capabilities in the evaluated AI model.

Technical Details: Underlying Technologies

• Reinforcement Learning Techniques:
  • Reasoning-Focused RL: DeepSeek utilizes a large-scale reinforcement learning approach specifically designed and optimized for reasoning tasks.
  • Rule-Based Reward System: Researchers behind DeepSeek developed a rule-based reward system for training the model, which has proven to outperform neural reward models commonly used in reinforcement learning.
  • Group Relative Policy Optimization (GRPO) Algorithm: DeepSeek employs their proposed Group Relative Policy Optimization (GRPO) algorithm in their reinforcement learning process, particularly in models like DeepSeekMath-RL 7B.
  • Proximal Policy Optimization (PPO): Proximal Policy Optimization (PPO) is mentioned as a foundational reinforcement learning (RL) algorithm used for training intelligent agents, likely within the broader context of DeepSeek’s RL strategy.
• Multi-head Latent Attention (MLA): Efficient Attention Mechanism:
  • MLA as a Multi-head Attention Variant: Multi-head Latent Attention (MLA) is identified as a variant of multi-head attention, introduced in the DeepSeek-V2 paper.
  • KV-Cache Size Reduction: The primary purpose of MLA and related multi-head attention variants is to reduce the KV-cache size.
  • Addressing Memory Bottleneck: Reducing KV-cache size is crucial for scaling large models as it mitigates the memory bottleneck associated with large language models.

V. Ethical and Legal Considerations: Risks and Concerns

• Potential Data Issues: Privacy and Security:
  • Data Collection: DeepSeek gathers technical information about user devices, including IP addresses, raising privacy considerations.
  • Security Risks: Despite its advancements, DeepSeek, like other AI platforms, still poses significant security risks that need to be addressed.
• Potential Copying: Plagiarism Allegations:
  • Distillation Hypothesis: Research suggests the hypothesis that DeepSeek could be a distilled version of ChatGPT, implying potential unauthorized copying or derivation.
  • OpenAI and Microsoft Investigation: Bloomberg and the BBC have reported that OpenAI and Microsoft are investigating whether OpenAI technology was utilized or acquired without authorization in relation to DeepSeek’s development.
  • Data Harvesting Accusations: Accusations have surfaced suggesting that DeepSeek may have “harvested ChatGPT for training data”, further fueling plagiarism concerns.
• Bans: Government Restrictions:
  • Australian Government Ban: The Artificial intelligence (AI) platform DeepSeek has been banned on all federal government systems and devices in Australia due to identified security and privacy concerns.

Additional Context

• DeepSeek AI (Cryptocurrency):
  • Cryptocurrency Listing: There appears to be a cryptocurrency named “DeepSeek AI” (Ticker: DEEPSEEK). It is noted that in the last 24 hours its price has changed by +0.86% or +6.24% (sources vary slightly), and that the 24-hour trading volume is $1,418.52 USD.
  • Price and Market Cap: The live price is reported as less than $0.000001 per DEEPSEEK/USD, with a current market cap of $0 USD and a circulating supply of 0.
  • Price Volatility: The cryptocurrency price is noted to be -53.22% from its 7-day all-time high and 149.17% from its 7-day all-time low, indicating significant volatility.
• Investment in DeepSeek (Company):
  • Private Company: DeepSeek (the AI company) is not publicly traded. Therefore, purchasing its stock is not available to the general public.
  • Accredited Investors: To buy stock in private companies like DeepSeek, one must be an accredited investor, meeting specific financial criteria required for private investment opportunities.