BestBlogs.dev Highlights Issue #32

DeepSeek officially released DeepSeek-R1, a large language model that matches OpenAI o1's performance in mathematics, coding, and natural language reasoning. DeepSeek-R1 leverages reinforcement learning in post-training, significantly improving reasoning capabilities. DeepSeek open-sourced the model weights and provides API services, allowing users to access chain-of-thought (CoT) outputs via model='deepseek-reasoner'. Model distillation yielded six smaller models; the 32B and 70B models rival OpenAI o1-mini's capabilities. Using the MIT License, DeepSeek explicitly allows model distillation, fostering open-source community growth. DeepSeek-R1's API pricing is 1 yuan per million input tokens (cache hit) / 4 yuan (cache miss), and 16 yuan per million output tokens.

DeepSeek-V3, DeepSeek's latest self-developed MoE model, features 671B parameters with 37B active parameters and was pre-trained on 14.8T tokens. It significantly outperformed other open-source models like Qwen2.5-72B and Llama-3.1-405B in various evaluations, achieving performance comparable to leading closed-source models such as GPT-4o and Claude-3.5-Sonnet. DeepSeek-V3 demonstrates substantial improvements in encyclopedic knowledge, long-form text generation, code, mathematics, and Chinese language processing. Algorithmic and engineering advancements have boosted generation speed from 20 TPS to 60 TPS, enhancing user experience. The API service pricing has been revised, with a 45-day discounted trial period available. DeepSeek-V3's native FP8 weights are open-sourced, supporting inference frameworks including SGLang, LMDeploy, TensorRT-LLM, and MindIE, encouraging community contributions and expanding application scenarios. DeepSeek plans to continue building upon the DeepSeek-V3 base model and share its ongoing research with the community.

This article delves into DeepSeek's newly released open-source multimodal model, Janus-Pro. Its innovative dual-encoder architecture, with separate encoders for image understanding and generation, overcomes the performance limitations of traditional unified models. The article details Janus-Pro's design and its three-stage training method, including locked-parameter adapter training, real-world data training, and optimized data ratios. Janus-Pro-7B achieves state-of-the-art results on MMBench (multimodal understanding) and GenEval (image generation) benchmarks, outperforming models like DALL-E 3 and Stable Diffusion. The article also explores the significance of Janus-Pro's architecture for the future of multimodal models and highlights the Transformer's crucial role in information integration.

DeepSeek's new model, DeepSeek-R1-Preview, achieves top performance in the LiveCodeBench code benchmark, rivaling OpenAI o1 and slated for open-source release. An upgrade to DeepSeek-R1-Lite-Preview, it utilizes a larger base model, resulting in enhanced reasoning capabilities. The DeepSeek team collaborated with LiveCodeBench to resolve scoring system bugs and demonstrate the model's reasoning process. The developer community eagerly anticipates the open-source model and API, expecting it to significantly streamline programming workflows. The article also covers updates on other Chinese-developed large language models and recent OpenAI developments.

This article presents a comprehensive blueprint for the emerging field of Reasoning Language Models (RLMs), emphasizing their crucial role as a stepping stone towards Artificial General Intelligence (AGI). It distinguishes the fundamental differences between RLMs and traditional Large Language Models (LLMs) in reasoning capabilities: RLMs possess a more advanced "System 2 thinking" ability, enabling them to perform extrapolation and solve complex problems. The article details the modular architecture of RLMs, including reasoning schemes, operators, models, training paradigms, and workflows. It provides a toolbox of components for building and evaluating RLMs. Furthermore, it explores RLM training methods, evaluation standards, and their connection to existing structured prompting schemes. Finally, it validates the blueprint's effectiveness using the Framework X1 example and looks ahead at the potential and applications of RLMs in driving AI technological advancements.

DeepSeek introduces Janus-Pro, a newly open-sourced multimodal model designed for both visual understanding and image generation. Janus-Pro significantly improves upon previous models in image generation quality, directly competing with and even surpassing OpenAI's DALL-E 3 in certain aspects. Its functionalities include image recognition, landmark recognition, text recognition, and image generation. The core technology, decoupled visual encoding (a technique separating visual encoding into distinct understanding and generation pathways), allows for superior performance in both areas. These improvements stem from optimized training strategies, extensive training data, and a larger model parameter count. DeepSeek's continued commitment to open-source initiatives underscores its dedication to advancing AI technology through collaboration.

This article delves into DeepSeek's recently released open-source model, R1, highlighting its innovative approach and significant technical breakthroughs. DeepSeek R1's core innovation lies in its utilization of pure reinforcement learning, enabling the spontaneous emergence of powerful reasoning abilities—a stark contrast to traditional methods reliant on supervised fine-tuning and complex reward models. The R1-Zero variant, trained with only simple accuracy and formatting rewards, demonstrates an 'Aha Moment'-like learning capacity and exceptional cross-domain transfer learning, achieving outstanding results in mathematics (AIME) and programming (Codeforces) competitions. While R1-Zero presents some readability challenges, its impressive reasoning potential is undeniable. The refined R1 model retains its strong reasoning capabilities while improving output readability, rivaling the performance of OpenAI's o1 model. DeepSeek R1's success strongly suggests the immense potential of pure reinforcement learning in fostering AI's innate reasoning abilities and paving the way toward AGI.

This article delves into the technical optimizations in the DeepSeek-V3 paper, particularly how DeepSeek bypassed NVIDIA's CUDA to directly use the PTX programming language for hardware efficiency optimization. The article highlights that DeepSeek achieved 10x computational efficiency over companies like Meta by reconfiguring the GPU's Streaming Multiprocessors (SMs). It also thoroughly analyzes the complexity of PTX programming, emphasizing its challenges in portability and cross-GPU architecture compatibility. Furthermore, the article discusses DeepSeek's challenge to NVIDIA's technological moat and the potential of AI self-optimization, suggesting that AI might start optimizing its own low-level code.

DeepSeek-V3 is a high-performance, low-cost open-source large language model (LLM) demonstrating superior performance across various benchmark tests, particularly in advanced mathematical reasoning, significantly outperforming other models. Its architectural innovations include Multi-head Latent Attention, DeepSeekMoE, and a lossless load balancing strategy, substantially improving model performance and efficiency. Engineering optimizations, such as DualPipe pipeline parallelism, communication optimization, memory management, and FP8 low-precision training, significantly enhanced training efficiency and GPU utilization. The training strategy involved meticulous data construction, tokenizer optimization, model configuration, and hyperparameter tuning, boosting performance in mathematics, programming, and multilingual processing. Further improvements in training efficiency and long-text processing were achieved through lossless load balancing, long context extension, and multi-token prediction. Post-training involved supervised fine-tuning and reinforcement learning for further performance optimization. DeepSeek-V3 achieved performance comparable to top-tier models at a cost of approximately $5.5 million, showcasing exceptional cost efficiency.

This article details how DeepSeek-V3 dramatically reduces the cost and time of large model training using advanced compression and optimization techniques. DeepSeek-V3 leverages a Multi-Layer Attention (MLA) architecture, an FP8 mixed-precision training framework, and the innovative DualPipe method to enhance training speed and efficiency while minimizing memory consumption and communication overhead. DeepSeek-V3 demonstrates superior performance in mathematical reasoning, code generation, and long text processing, although its capabilities in creative generation and open-ended tasks are relatively limited. Its success stems from large-scale parameters, fine-grained data processing, multi-token prediction techniques, and R1 distillation. The article highlights DeepSeek-V3's engineering innovations, showcasing a successful balance between theoretical advancements and practical implementation.

This Hugging Face blog post introduces the Open-R1 project, a community-driven initiative to replicate DeepSeek-R1, a powerful reasoning model recently released by DeepSeek. DeepSeek-R1 demonstrated impressive performance in reasoning tasks using reinforcement learning without human supervision, building upon the strong DeepSeek-V3 base model. While DeepSeek released model weights and a tech report, the datasets and training code remain closed. Open-R1 seeks to address this gap by reconstructing the data and training pipeline, focusing on distilling reasoning datasets from DeepSeek-R1, replicating the pure RL pipeline, and demonstrating a multi-stage training approach from base model to RL. The project aims to provide transparency, reproducibility, and a foundation for the community to collaboratively advance open reasoning models, inviting contributions to code and discussions to build this together.

SiliconFlow and Huawei Cloud jointly launch DeepSeek R1 and V3 inference services based on Ascend Cloud. Leveraging Huawei Cloud's Ascend Cloud's powerful computing power and SiliconFlow's self-developed inference acceleration engine, the performance is comparable to globally high-end GPU-deployed models. The service has five key features: high-performance inference based on Ascend Cloud, stable production-level service, no deployment hassles, promotional pricing, and alignment with DeepSeek's official pricing. Additionally, the SiliconCloud platform offers a wide selection of models, including the DeepSeek series, Qwen2.5, Llama-3.3, and over 20 other open-source large models, with some model APIs available for free, helping developers reduce R&D costs and achieve cost efficiency in token usage. The article also provides online experience links and API documentation to help developers get started quickly.

This technical guide details deployment strategies for DeepSeek-R1 models - open-source alternatives to OpenAI's reasoning-optimized architectures - across AWS infrastructure. It covers: 1) Serverless deployment via Hugging Face Inference Endpoints ($8.3/hr) 2) SageMaker configurations for GPU/Neuron instances with hardware recommendations 3) EC2 deployment using Hugging Face's Neuron DLAMI. The article emphasizes cost optimization through six distilled model variants (70B to 1.5B parameters) and AWS-specific optimizations like pre-compiled models for Inferentia chips. While deployment workflows are fully documented with code samples, fine-tuning implementation remains under development.

The article provides a detailed guide on deploying DeepSeek-R1 distilled Llama models using Amazon Bedrock's Custom Model Import feature. It explains the distillation process, which involves training smaller models to mimic the behavior of larger models, improving inference speed and reducing computational costs. The article also covers the steps for importing and deploying these models, including prerequisites, model preparation, and testing. Additionally, it highlights the cost efficiency and scalability benefits of using Amazon Bedrock for model deployment.

The article delves into DeepSeek's core features and application techniques, particularly its unique advantages as a reasoning-focused large model. By comparing it with traditional instruction-focused large models, it demonstrates DeepSeek's flexibility and efficiency in handling complex tasks. The article also provides tips on how to elicit the best performance from DeepSeek through clear and concise requirement descriptions, and highlights its innovative features such as text style transfer and deep thinking. While DeepSeek has significant advantages in improving work efficiency, the article also points out its limitations in handling long texts and sensitive content, reflecting a comprehensive evaluation of DeepSeek.

This article delves into DeepSeek-R1, a reasoning model from the Chinese company DeepSeek. Its efficiency, low cost, and open-source nature have rapidly propelled it to prominence in the global AI community. The analysis covers DeepSeek-R1's background, features, applications, its distinctive 'no-technique' prompting methodology (favoring clear, concise language), exceptional Chinese writing capabilities, internet connectivity for real-time information access, and its impact on computing power demand. The article highlights DeepSeek-R1's superior reasoning abilities and the effectiveness of using plain language for interaction. It also explores how DeepSeek's low cost is driving broader AI adoption, consequently increasing overall computing power needs, a phenomenon consistent with Jevons Paradox. Finally, the article connects DeepSeek's success to China's technological advancements and national development, viewing it as a significant symbol of progress.

This article introduces DeepSeek-R1, a novel AI model from the Chinese startup DeepSeek, noting its comparable performance to models like OpenAI's o1 but at a lower cost. It highlights a freeCodeCamp course designed for developers and researchers to learn DeepSeek-R1. The course covers the model's architecture, training with Group Relative Policy Optimization (GRPO), and hands-on deployment using tools like Ollama, LMStudio, and Hugging Face Transformers. By emphasizing practical application, the course aims to enable users to leverage DeepSeek-R1's advanced reasoning in their projects and promotes the open-source model's impact on democratizing AI research and application.

This article demonstrates how to run the DeepSeek-R1 model locally using the Ollama tool, covering detailed steps from installation and configuration to model execution. It emphasizes the privacy protection and customization advantages of on-premise AI model deployment, along with practical application examples. Finally, the article explores the potential of DeepSeek-R1 in the AI field, particularly its value in enhancing privacy protection and delivering customized services.

DeepSeek R1 is now accessible in the model catalog on Azure AI Foundry and GitHub, joining over 1,800 diverse AI models. This platform allows businesses to integrate advanced AI seamlessly while maintaining security and responsible AI commitments. DeepSeek R1 offers a cost-efficient model, accelerating AI reasoning for developers and enterprises with minimal infrastructure investment. The Azure AI Foundry provides built-in model evaluation tools for rapid experimentation and integration. Additionally, DeepSeek R1 has undergone rigorous safety evaluations to ensure a secure environment for deploying AI solutions.

In an interview, DeepSeek founder Liang Wenfeng shared profound insights into the development of AI in China, emphasizing that China must stand at the technological frontier and avoid forever following. DeepSeek, a leading AI research company in China, has triggered a significant price competition in the large model market by releasing cost-effective open-source models V3 and V2, which have performed excellently in multiple evaluations, approaching the levels of GPT-4o and Claude 3.5 Sonnet. Liang Wenfeng stressed that DeepSeek's goal is to promote groundbreaking innovation rather than simple commercialization. He mentioned the importance of open-source and team growth, believing that open-source is more of a cultural behavior than a commercial one. DeepSeek's AI research is not limited to quantitative investment but focuses more on the overall description of financial markets and paradigm exploration. The company adopts a bottom-up innovation model, encouraging employees to proactively propose ideas and flexibly allocate resources. Liang Wenfeng believes that innovation requires confidence, and top talent in China is undervalued; solving the hardest problems is the way to attract them. He also shared Unique Ideation's unique philosophy in recruitment and management, emphasizing ability over experience and the need for freedom and trial opportunities in innovation. Liang Wenfeng believes that the future large model market will feature specialized divisions, with foundational models and services provided by specialized companies. Innovation is spontaneous, not deliberately arranged, and DeepSeek focuses more on building a technology ecosystem rather than short-term application development.

Following the release of its iOS app, DeepSeek swiftly ascended to the top of App Stores in both the US and China, eclipsing ChatGPT and generating significant interest and a surge of reproduction efforts within the AI community. This article delves into the remarkable rise of the DeepSeek-R1 model and the importance of its reproduction, analyzing the challenges involved, such as intricate training process details, data generation, and demanding hardware requirements. Hugging Face initiated the Open R1 project, aiming for a fully open-source reproduction of DeepSeek-R1. Meanwhile, teams from the Hong Kong University of Science and Technology and TinyZero independently achieved partial replications of R1 using 7B and 3B models with limited datasets, highlighting the impressive potential of the DeepSeek model. DeepSeek's success has prompted apprehension at Meta and other industry giants, leading them to accelerate their analysis and response, potentially influencing their future AI strategies. This article underscores the innovative nature and impact of DeepSeek-R1, signaling a paradigm shift in the landscape of large AI models.

This article presents an expert interview with Professor Zhai Jidong from Tsinghua University's Department of Computer Science, exploring key strategies for optimizing AI computing power in the era of large language models. Using DeepSeek as a case study, it reveals how the synergistic innovation of algorithms and system software enabled the training of top-tier models with limited computing resources, challenging the conventional wisdom of prioritizing sheer computing power. Crucially, it highlights that given the hardware disparities between China and the US, China's AI development should prioritize independent innovation, focusing on system software and integrated hardware-software optimization to build a complete ecosystem spanning applications to chips, thereby achieving a breakthrough in computing capabilities. The interview covers various aspects, including computing resource utilization assessment, hardware-software adaptation strategies, the challenges of large-scale GPU clusters, and future trends in computing power, offering valuable insights and guidance for the advancement and sustainable development of China's AI industry.

DeepSeek has unveiled DeepSeek-R1, a powerful large language model (LLM) designed for complex logical reasoning prior to output generation. Its code and weights are openly licensed for both commercial and personal use. Built upon DeepSeek-V3-Base, DeepSeek-R1 underwent four stages of fine-tuning, employing a Mixture-of-Experts architecture with a total of 671 billion parameters. Training involved a synthetic dataset comprising thousands of long-form reasoning chains, leveraging the Group Relative Policy Optimization reinforcement learning algorithm. DeepSeek-R1 demonstrated exceptional performance across various benchmarks, surpassing OpenAI's o1 model in several instances. DeepSeek also released related models, including DeepSeek-R1-Zero and several dense models. The model's transparent reasoning process and open license establish it as a significant contribution to the open-source LLM landscape, with potential applications in model distillation.

This article recounts a closed-door meeting on DeepSeek, organized by Shixiang, bringing together leading AI researchers, investors, and practitioners. The discussion focused on the technical underpinnings, organizational culture, and far-reaching influence of DeepSeek-R1, whose rapid adoption unexpectedly captivated the global AI community. Topics included founder Liang Wenfeng's technical insights, DeepSeek's efficient reasoning models and data distillation techniques, computing power considerations, organizational culture, and the transformative impact of its open-source strategy. Key technical details such as Supervised Fine-Tuning (SFT) and distillation were thoroughly examined. Experts concluded that DeepSeek's success stems not only from technological innovation but also from its unwavering commitment to advancing intelligence itself, prioritizing long-term goals over short-term commercial gains, and its open-source approach to fostering AI accessibility and development. The article further analyzes the contrasting development trajectories and computing power needs of AI followers and pioneers, and offers perspectives on future AI trends, including novel architectures, multimodal applications, and enhanced computing power efficiency. Ultimately, the article underscores the critical role of a clear, long-term vision in navigating the complexities of AI development, surpassing the importance of technology itself, and concludes with insightful reflections on DeepSeek's future and the broader AI landscape.

The article systematically analyzes the impact of Chinese AI company DeepSeek's breakthrough on U.S. technological superiority, revealing three core technological dynamics: the scaling law determines the performance growth curve, a 4x annual cost reduction curve shift accelerates technological iteration, and the reinforcement learning paradigm shift creates a window of opportunity. It points out that DeepSeek-V3 achieves a 3-4x cost reduction through optimized hybrid expert architecture, and its mixed acquisition of 50,000 Hopper chips (including smuggled H100s, pre-ban H800 inventory, and H20 licenses) confirms the effectiveness of export controls. It predicts that 2026-2027 will be a critical juncture in determining a unipolar or bipolar world order, advocating for continued strengthening of chip controls (preventing China from acquiring millions of chips) to ensure U.S. technological superiority translates into strategic advantage, particularly emphasizing that the current period is a strategic opportunity in the scaling curve of the reinforcement learning paradigm.

DeepSeek V3, a Mixture of Experts (MoE) model with 671 billion parameters and 37 billion active parameters, was pre-trained on 14.8 trillion high-quality tokens. It outperforms open-source models like Llama 3.1 405B on multiple benchmarks and rivals top closed-source models such as GPT-4o and Claude 3.5 Sonnet. Its remarkably low training cost of $5.576 million, significantly less than comparable models, and its competitively priced API make it a game-changer. The article details DeepSeek V3's architectural optimizations, training strategies, and performance, highlighting its efficiency in resource-constrained environments and its innovative approach to distributed inference and load balancing.

This article, compiled by Machine Heart, compares DeepSeek's R1 reasoning model against OpenAI's ChatGPT o1 and o1 Pro across eight common usage scenarios. These scenarios included creative writing, mathematics, and instruction following, simulating real-world user experiences. DeepSeek R1 excelled in tasks like dad jokes (simple, often pun-based jokes, typically considered corny or slightly awkward), creative storytelling, large prime number queries, and flight time planning. However, it showed some weaknesses in unusual acrostic poems and complex set testing. The evaluation highlights DeepSeek R1's high performance at a fraction of the cost of OpenAI's paid models, proving that a cost-effective approach can be highly competitive in the AI field.

This article details the widespread reproduction of the DeepSeek R1 model. Numerous individuals and institutions have successfully replicated its key features—including emergent reasoning and self-correction mechanisms—using reinforcement learning, at a remarkably low cost (approximately $30). This achievement highlights how reinforcement learning enables models to develop self-correcting and search strategies during training, resulting in superior performance on complex tasks. This contrasts sharply with the traditional reliance on massive computational resources, suggesting that high-performance large language models are no longer the exclusive domain of major corporations. Open-source and low-cost approaches are now viable alternatives. Expert opinions suggest that DeepSeek R1's success could significantly impact the technological advantages and valuations of Silicon Valley AI giants, marking a pivotal shift towards democratized access to powerful AI models. HuggingFace's involvement and open-sourcing of related processes further accelerates this technological democratization. The article also notes DeepSeek R1's growing popularity and influence within the developer community.

The article delves into the rapid release of the DeepSeek R1 Model and its deployment on multiple AI platforms, particularly its promotion on platforms like Microsoft and Amazon, which has garnered significant attention. Through innovative GPU optimization techniques and efficient inference capabilities, DeepSeek has significantly reduced AI inference costs, challenging the traditional notion that AI models rely on expensive hardware. The article also explores controversies surrounding DeepSeek, including whether it has improperly used OpenAI data and how it circumvents GPU chip limitations. Additionally, the article analyzes the long-term impact of the DeepSeek Model on the AI industry, particularly its potential to alter the business models of companies like Microsoft, Amazon, and Meta.

The article introduces the technological breakthrough of DeepSeek's R1 Model in the AI field. The R1 Model has achieved competitive capabilities with top-tier AI models at a cost lower than Silicon Valley giants, and through innovative engineering design and model distillation (a technique to compress large models into smaller ones), it has challenged the prevailing belief that larger models are inherently superior in AI development. DeepSeek's breakthrough has not only made the AI industry reconsider cost and scale but also prompted companies like OpenAI to accelerate the release of new models, proving that finely trained smaller models can also achieve great success. The article delves into how the R1 Model enhances reasoning capabilities through reinforcement learning and multi-step reasoning, showcasing the profound impact of the AI industry on technology and applications.

This article analyzes Meta Platforms' concerns regarding the rise of DeepSeek, a powerful AI model developed by the Chinese quantitative hedge fund, Magic Square (幻方). DeepSeek's exceptional performance and cost-effectiveness, particularly as an open-source model, directly challenge Meta's Llama series and its commercialization strategy. DeepSeek rivals or surpasses top models from OpenAI and Meta in benchmark tests, yet boasts significantly lower development and operational costs, questioning the efficiency of current high-cost AI development models. In response, Meta has established multiple task forces to thoroughly analyze DeepSeek, aiming to integrate its technical advantages into future Llama model improvements. The article further explores DeepSeek's impact on the open-source AI ecosystem, the global AI competitive landscape, and potential political and commercial ramifications. DeepSeek's emergence accelerates open-source AI development and compels industry giants to reassess their AI strategies and resource allocation.

The article provides a detailed analysis of DeepSeek's technological innovations in the AI field, especially its breakthroughs in reducing model training and inference costs. It highlights the key advancements of the V3 and R1 models, such as DeepSeekMoE and DeepSeekMLA, which enhance inference efficiency while lowering costs. The article further explores the far-reaching impact of DeepSeek's strategic open-source approach on large companies and the market, with enterprises like Microsoft, Apple, Meta, and Amazon benefiting from low-cost inference. It also analyzes the impact of the US-imposed chip export ban on DeepSeek's development path, showcasing the rise of Chinese tech companies in the global AI competition and potential future shifts in the technological landscape.

This article provides an in-depth analysis of the rise of the Chinese AI company DeepSeek and its impact on global AI competition. DeepSeek has broken through traditional AI bottlenecks by adopting low-cost and low-energy technologies, avoiding reliance on expensive hardware, and using open-source strategies to provide cost-effective AI services, successfully challenging the market dominance of American tech giants. The article focuses on DeepSeek's market responses to companies like OpenAI and Anthropic, as well as its potential impact on the American tech capitalism model. Additionally, the article analyzes how DeepSeek's success is reshaping the global AI market landscape and forcing American tech giants to adjust their technology and market strategies.

The article delves into the disputes among DeepSeek, OpenAI, and Anthropic, particularly OpenAI's accusations that DeepSeek infringed on intellectual property rights and allegedly distilled its models without authorization. It also discusses the widespread application of model distillation technology in generative AI and analyzes how DeepSeek is driving the diversification of generative AI applications through optimized cost control. DeepSeek counters OpenAI's accusations by pointing out OpenAI's own compliance issues and explores DeepSeek's technological breakthroughs and market impact. The article also discusses how DeepSeek's pricing strategy provides new directions for AI computing power demand and return on investment.

This article explores how DeepSeek has captured global attention in the AI field by open-sourcing its R1 model and providing highly affordable API pricing. DeepSeek's R1 model matches the performance of OpenAI's o1 model, but its open-source approach and cost advantages have attracted significant interest from developers and research teams. The article explains key AI concepts like 'training' and 'inference' using a chef-cooking analogy for clarity. DeepSeek's breakthrough in the inference phase has significantly reduced computational power and costs, positioning it as a standout player in the global AI race. However, DeepSeek still faces challenges in engineering capabilities and service stability, requiring a smooth transition from research to market. The article concludes that DeepSeek's success introduces a new dynamic to the global AI competition, though its future growth depends on overcoming existing bottlenecks.

DeepSeek has gained significant recognition for its groundbreaking achievements in the AI field, particularly its DeepSeek-v3 large language model. This model outperforms Llama 3 405B with a remarkable 1/11th of the computational resources. The team's core members are predominantly graduates from prestigious Chinese universities, who have spearheaded key innovations such as Multi-head Latent Attention (MLA) and Group Relative Policy Optimization (GRPO). These advancements not only drastically reduce computational costs but also significantly improve model performance. DeepSeek's organizational structure mirrors OpenAI's, emphasizing a youthful, innovation-driven culture with flexible resource allocation. This, coupled with its unique software-hardware co-design strategy, positions DeepSeek as a leading force in China's AI sector.

As a leading example of China's AI large models, DeepSeek has significantly lowered AI training costs by leveraging technologies like Mixture of Experts (MoE) and Reinforcement Learning (RL), earning the title of 'the budget-friendly option in the large model space.' Its open source strategy has not only attracted a global developer ecosystem but also achieved commercialization through the Dual Code Model, Insurance Fee Model, and Cloud Service Model. DeepSeek's success has shaken Silicon Valley, even being compared to a 'Sputnik Moment,' symbolizing China's rise in the AI field. However, its technical approach has also sparked discussions on 'distillation' and intellectual property disputes. The article also explores the importance of AI technology to national competitiveness and the prospects of competition and cooperation between the US and China in the AI field.

DeepSeek is an open-source AI model developed in China, recently garnering global attention. While its low cost and high performance have attracted tech giants like NVIDIA, Intel, and Microsoft, it has also faced resistance and blockades from governments and enterprises in Europe and the US due to privacy and security concerns. The US military, Italy, Ireland, and others have taken measures to restrict its use, and cybersecurity firms have warned of its potential exploitation by hackers. However, DeepSeek's open-source model is seen as revolutionary, promoting the democratization of AI technology, lowering development barriers, and providing innovation opportunities for small and medium-sized enterprises. Indian IT Minister, Apple CEO Tim Cook, and AI expert Andrew Ng have acknowledged its innovative potential, suggesting it could profoundly impact the China-US AI competition and even lead the global AI infrastructure wave.