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Company Description
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DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese artificial intelligence business that establishes open-source large language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and moneyed by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the business in 2023 and works as its CEO.
The DeepSeek-R1 design offers responses similar to other modern big language designs, such as OpenAI’s GPT-4o and o1. [1] It is trained at a significantly lower cost-stated at US$ 6 million compared to $100 million for OpenAI’s GPT-4 in 2023 [2] -and needs a tenth of the computing power of a comparable LLM. [2] [3] [4] DeepSeek’s AI designs were established amid United States sanctions on India and China for Nvidia chips, [5] which were intended to limit the capability of these 2 nations to establish innovative AI systems. [6] [7]
On 10 January 2025, DeepSeek released its very first free chatbot app, based upon the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had gone beyond ChatGPT as the most-downloaded complimentary app on the iOS App Store in the United States, [8] causing Nvidia’s share rate to come by 18%. [9] [10] DeepSeek’s success versus larger and more recognized competitors has been referred to as “upending AI”, [8] making up “the first chance at what is becoming a global AI area race”, [11] and ushering in “a brand-new age of AI brinkmanship”. [12]
DeepSeek makes its generative expert system algorithms, designs, and training information open-source, allowing its code to be easily offered for usage, modification, watching, and creating files for developing purposes. [13] The business supposedly vigorously recruits young AI researchers from top Chinese universities, [8] and hires from outside the computer technology field to diversify its designs’ knowledge and abilities. [3]
In February 2016, High-Flyer was co-founded by AI lover Liang Wenfeng, who had actually been trading considering that the 2007-2008 monetary crisis while going to Zhejiang University. [14] By 2019, he developed High-Flyer as a hedge fund focused on developing and using AI trading algorithms. By 2021, High-Flyer exclusively utilized AI in trading. [15] DeepSeek has made its generative synthetic intelligence chatbot open source, suggesting its code is easily readily available for use, modification, and viewing. This consists of permission to gain access to and utilize the source code, along with design files, for constructing purposes. [13]
According to 36Kr, Liang had actually developed a shop of 10,000 Nvidia A100 GPUs, which are used to train AI [16], before the United States federal government enforced AI chip restrictions on China. [15]
In April 2023, High-Flyer started an artificial basic intelligence lab dedicated to research establishing AI tools separate from High-Flyer’s financial business. [17] [18] In May 2023, with High-Flyer as one of the investors, the laboratory became its own business, DeepSeek. [15] [19] [18] Venture capital companies hesitated in supplying funding as it was not likely that it would be able to produce an exit in a brief amount of time. [15]
After releasing DeepSeek-V2 in May 2024, which offered strong performance for a low rate, DeepSeek became referred to as the catalyst for China’s AI design price war. It was rapidly called the “Pinduoduo of AI“, and other major tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the cost of their AI models to complete with the business. Despite the low price charged by DeepSeek, it was lucrative compared to its rivals that were losing cash. [20]
DeepSeek is focused on research study and has no comprehensive prepare for commercialization; [20] this also permits its innovation to prevent the most strict provisions of China’s AI regulations, such as needing consumer-facing technology to abide by the government’s controls on information. [3]
DeepSeek’s working with choices target technical capabilities instead of work experience, resulting in the majority of new hires being either current university graduates or developers whose AI professions are less established. [18] [3] Likewise, the business hires individuals with no computer technology background to assist its technology understand other subjects and understanding locations, including being able to produce poetry and perform well on the notoriously tough Chinese college admissions exams (Gaokao). [3]
Development and release history
DeepSeek LLM
On 2 November 2023, DeepSeek released its first series of model, DeepSeek-Coder, which is readily available free of charge to both scientists and business users. The code for the design was made open-source under the MIT license, with an extra license contract (“DeepSeek license”) relating to “open and accountable downstream usage” for the model itself. [21]
They are of the very same architecture as DeepSeek LLM detailed listed below. The series includes 8 models, 4 pretrained (Base) and 4 instruction-finetuned (Instruct). They all have 16K context lengths. The training was as follows: [22] [23] [24]
1. Pretraining: 1.8 T tokens (87% source code, 10% code-related English (GitHub markdown and Stack Exchange), and 3% code-unrelated Chinese).
2. Long-context pretraining: 200B tokens. This extends the context length from 4K to 16K. This produced the Base models.
3. Supervised finetuning (SFT): 2B tokens of guideline data. This produced the Instruct designs.
They were trained on clusters of A100 and H800 Nvidia GPUs, linked by InfiniBand, NVLink, NVSwitch. [22]
On 29 November 2023, DeepSeek launched the DeepSeek-LLM series of models, with 7B and 67B specifications in both Base and Chat types (no Instruct was released). It was developed to compete with other LLMs offered at the time. The paper claimed benchmark results greater than a lot of open source LLMs at the time, specifically Llama 2. [26]: area 5 Like DeepSeek Coder, the code for the model was under MIT license, with DeepSeek license for the model itself. [27]
The architecture was essentially the like those of the Llama series. They used the pre-norm decoder-only Transformer with RMSNorm as the normalization, SwiGLU in the feedforward layers, rotary positional embedding (RoPE), and grouped-query attention (GQA). Both had vocabulary size 102,400 (byte-level BPE) and context length of 4096. They trained on 2 trillion tokens of English and Chinese text obtained by deduplicating the Common Crawl. [26]
The Chat variations of the 2 Base designs was likewise released concurrently, gotten by training Base by monitored finetuning (SFT) followed by direct policy optimization (DPO). [26]
On 9 January 2024, they launched 2 DeepSeek-MoE models (Base, Chat), each of 16B criteria (2.7 B triggered per token, 4K context length). The training was basically the like DeepSeek-LLM 7B, and was trained on a part of its training dataset. They declared equivalent performance with a 16B MoE as a 7B non-MoE. In architecture, it is a version of the basic sparsely-gated MoE, with “shared professionals” that are always queried, and “routed professionals” that may not be. They discovered this to aid with expert balancing. In basic MoE, some professionals can end up being extremely relied on, while other experts may be seldom used, wasting criteria. Attempting to balance the specialists so that they are equally utilized then triggers professionals to replicate the very same capability. They proposed the shared professionals to discover core capacities that are frequently utilized, and let the routed professionals to discover the peripheral capacities that are rarely used. [28]
In April 2024, they launched 3 DeepSeek-Math models specialized for doing mathematics: Base, Instruct, RL. It was trained as follows: [29]
1. Initialize with a previously pretrained DeepSeek-Coder-Base-v1.5 7B.
2. Further pretrain with 500B tokens (6% DeepSeekMath Corpus, 4% AlgebraicStack, 10% arXiv, 20% GitHub code, 10% Common Crawl). This produced the Base design.
3. Train an instruction-following design by SFT Base with 776K mathematics problems and their tool-use-integrated step-by-step solutions. This produced the Instruct model.
Reinforcement learning (RL): The reward model was a process benefit model (PRM) trained from Base according to the Math-Shepherd approach. [30] This reward model was then used to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K mathematics concerns “related to GSM8K and MATH”. The reward design was continuously updated during training to avoid benefit hacking. This resulted in the RL design.
V2
In May 2024, they released the DeepSeek-V2 series. The series consists of 4 models, 2 base models (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 bigger models were trained as follows: [31]
1. Pretrain on a dataset of 8.1 T tokens, where Chinese tokens are 12% more than English ones.
2. Extend context length from 4K to 128K using YaRN. [32] This led to DeepSeek-V2.
3. SFT with 1.2 M circumstances for helpfulness and 0.3 M for security. This resulted in DeepSeek-V2-Chat (SFT) which was not launched.
4. RL utilizing GRPO in two phases. The very first stage was trained to fix math and coding issues. This stage utilized 1 reward design, trained on compiler feedback (for coding) and ground-truth labels (for math). The 2nd stage was trained to be handy, safe, and follow guidelines. This phase used 3 reward models. The helpfulness and safety benefit designs were trained on human preference data. The rule-based benefit design was by hand set. All experienced reward models were initialized from DeepSeek-V2-Chat (SFT). This resulted in the launched variation of DeepSeek-V2-Chat.
They selected 2-staged RL, due to the fact that they found that RL on thinking information had “special characteristics” different from RL on basic information. For example, RL on thinking might improve over more training actions. [31]
The two V2-Lite models were smaller sized, and experienced likewise, though DeepSeek-V2-Lite-Chat just underwent SFT, not RL. They trained the Lite variation to help “additional research and advancement on MLA and DeepSeekMoE”. [31]
Architecturally, the V2 designs were considerably modified from the DeepSeek LLM series. They altered the basic attention mechanism by a low-rank approximation called multi-head hidden attention (MLA), and utilized the mixture of specialists (MoE) alternative formerly published in January. [28]
The Financial Times reported that it was cheaper than its peers with a cost of 2 RMB for every million output tokens. The University of Waterloo Tiger Lab’s leaderboard ranked DeepSeek-V2 seventh on its LLM ranking. [19]
In June 2024, they released 4 models in the DeepSeek-Coder-V2 series: V2-Base, V2-Lite-Base, V2-Instruct, V2-Lite-Instruct. They were trained as follows: [35] [note 2]
1. The Base models were initialized from corresponding intermediate checkpoints after pretraining on 4.2 T tokens (not the version at the end of pretraining), then pretrained even more for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were used to produce 20K code-related and 30K math-related instruction information, then integrated with a guideline dataset of 300M tokens. This was used for SFT.
2. RL with GRPO. The benefit for math problems was calculated by comparing to the ground-truth label. The benefit for code issues was created by a reward model trained to anticipate whether a program would pass the system tests.
DeepSeek-V2.5 was released in September and updated in December 2024. It was made by combining DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. [36]
V3
In December 2024, they released a base model DeepSeek-V3-Base and a chat design DeepSeek-V3. The model architecture is essentially the exact same as V2. They were trained as follows: [37]
1. Pretraining on 14.8 T tokens of a multilingual corpus, mostly English and Chinese. It included a greater ratio of mathematics and shows than the pretraining dataset of V2.
2. Extend context length two times, from 4K to 32K and then to 128K, using YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 dates on 1.5 M samples of reasoning (mathematics, programming, logic) and non-reasoning (imaginative writing, roleplay, easy question answering) data. Reasoning data was produced by “skilled designs”. Non-reasoning data was created by DeepSeek-V2.5 and checked by humans. – The “expert designs” were trained by beginning with an undefined base design, then SFT on both information, and synthetic information created by an internal DeepSeek-R1 design. The system timely asked the R1 to show and verify during thinking. Then the expert designs were RL utilizing an undefined benefit function.
– Each specialist model was trained to generate just artificial thinking information in one specific domain (math, programs, reasoning).
– Expert models were utilized, instead of R1 itself, considering that the output from R1 itself suffered “overthinking, poor formatting, and excessive length”.
4. Model-based reward designs were made by beginning with a SFT checkpoint of V3, then finetuning on human choice information including both last reward and chain-of-thought resulting in the final reward. The reward model produced benefit signals for both concerns with unbiased but free-form responses, and concerns without unbiased responses (such as innovative writing).
5. A SFT checkpoint of V3 was trained by GRPO using both reward models and rule-based benefit. The rule-based reward was computed for math problems with a final answer (put in a box), and for programming issues by unit tests. This produced DeepSeek-V3.
The DeepSeek team performed substantial low-level engineering to achieve effectiveness. They utilized mixed-precision arithmetic. Much of the forward pass was carried out in 8-bit floating point numbers (5E2M: 5-bit exponent and 2-bit mantissa) rather than the basic 32-bit, requiring unique GEMM routines to build up precisely. They used a custom 12-bit float (E5M6) for only the inputs to the direct layers after the attention modules. Optimizer states remained in 16-bit (BF16). They lessened the communication latency by overlapping thoroughly computation and communication, such as devoting 20 streaming multiprocessors out of 132 per H800 for only inter-GPU interaction. They decreased communication by rearranging (every 10 minutes) the specific device each specialist was on in order to prevent particular devices being queried more frequently than the others, including auxiliary load-balancing losses to the training loss function, and other load-balancing strategies. [37]
After training, it was deployed on H800 clusters. The H800 cards within a cluster are connected by NVLink, and the clusters are connected by InfiniBand. [37]
Benchmark tests reveal that DeepSeek-V3 surpassed Llama 3.1 and Qwen 2.5 whilst matching GPT-4o and Claude 3.5 Sonnet. [18] [39] [40] [41]
R1
On 20 November 2024, DeepSeek-R1-Lite-Preview ended up being accessible by means of DeepSeek’s API, along with via a chat interface after logging in. [42] [43] [note 3] It was trained for sensible reasoning, mathematical thinking, and real-time analytical. DeepSeek claimed that it exceeded performance of OpenAI o1 on benchmarks such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal specified when it utilized 15 problems from the 2024 edition of AIME, the o1 design reached an option quicker than DeepSeek-R1-Lite-Preview. [45]
On 20 January 2025, DeepSeek launched DeepSeek-R1 and DeepSeek-R1-Zero. [46] Both were initialized from DeepSeek-V3-Base, and share its architecture. The company likewise released some “DeepSeek-R1-Distill” designs, which are not initialized on V3-Base, but instead are initialized from other pretrained open-weight designs, including LLaMA and Qwen, then fine-tuned on artificial data produced by R1. [47]
A discussion in between User and Assistant. The user asks a question, and the Assistant solves it. The assistant initially thinks about the reasoning process in the mind and after that provides the user with the response. The reasoning process and response are enclosed within and tags, respectively, i.e., thinking process here answer here. User:. Assistant:
DeepSeek-R1-Zero was trained exclusively using GRPO RL without SFT. Unlike previous versions, they used no model-based reward. All benefit functions were rule-based, “primarily” of 2 types (other types were not defined): accuracy benefits and format benefits. Accuracy reward was checking whether a boxed answer is right (for mathematics) or whether a code passes tests (for programming). Format reward was inspecting whether the design puts its thinking trace within … [47]
As R1-Zero has issues with readability and blending languages, R1 was trained to address these problems and more enhance thinking: [47]
1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” data all with the basic format of|special_token|| special_token|summary >.
2. Apply the very same RL procedure as R1-Zero, but likewise with a “language consistency benefit” to motivate it to react monolingually. This produced an internal design not released.
3. Synthesize 600K thinking information from the internal model, with rejection tasting (i.e. if the created thinking had a wrong last response, then it is gotten rid of). Synthesize 200K non-reasoning data (writing, factual QA, self-cognition, translation) using DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K artificial data for 2 epochs.
5. GRPO RL with rule-based benefit (for thinking jobs) and model-based reward (for non-reasoning jobs, helpfulness, and harmlessness). This produced DeepSeek-R1.
Distilled designs were trained by SFT on 800K data synthesized from DeepSeek-R1, in a similar way as step 3 above. They were not trained with RL. [47]
Assessment and responses
DeepSeek launched its AI Assistant, which uses the V3 design as a chatbot app for Apple IOS and Android. By 27 January 2025 the app had surpassed ChatGPT as the highest-rated free app on the iOS App Store in the United States; its chatbot reportedly responds to questions, fixes logic problems and composes computer programs on par with other chatbots on the marketplace, according to benchmark tests utilized by American AI companies. [3]
DeepSeek-V3 utilizes significantly less resources compared to its peers; for example, whereas the world’s leading AI companies train their chatbots with supercomputers using as numerous as 16,000 graphics processing units (GPUs), if not more, DeepSeek claims to require just about 2,000 GPUs, specifically the H800 series chip from Nvidia. [37] It was trained in around 55 days at a cost of US$ 5.58 million, [37] which is approximately one tenth of what United States tech huge Meta spent building its newest AI innovation. [3]
DeepSeek’s competitive efficiency at reasonably minimal expense has actually been acknowledged as potentially challenging the international supremacy of American AI designs. [48] Various publications and news media, such as The Hill and The Guardian, explained the release of its chatbot as a “Sputnik minute” for American AI. [49] [50] The performance of its R1 model was supposedly “on par with” among OpenAI’s latest designs when utilized for jobs such as mathematics, coding, and natural language reasoning; [51] echoing other analysts, American Silicon Valley investor Marc Andreessen likewise explained R1 as “AI’s Sputnik minute”. [51]
DeepSeek’s founder, Liang Wenfeng has actually been compared to Open AI CEO Sam Altman, with CNN calling him the Sam Altman of China and an evangelist for AI. [52] Chinese state media commonly applauded DeepSeek as a nationwide possession. [53] [54] On 20 January 2025, China’s Premier Li Qiang welcomed Liang Wenfeng to his seminar with professionals and asked him to supply viewpoints and recommendations on a draft for comments of the annual 2024 government work report. [55]
DeepSeek’s optimization of minimal resources has actually highlighted prospective limitations of United States sanctions on China’s AI advancement, which include export limitations on innovative AI chips to China [18] [56] The success of the business’s AI designs consequently “stimulated market chaos” [57] and triggered shares in major international innovation business to plunge on 27 January 2025: Nvidia’s stock fell by as much as 17-18%, [58] as did the stock of competing Broadcom. Other tech companies likewise sank, including Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip devices maker ASML (down over 7%). [51] A worldwide selloff of innovation stocks on Nasdaq, triggered by the release of the R1 design, had resulted in tape-record losses of about $593 billion in the market capitalizations of AI and computer hardware business; [59] by 28 January 2025, a total of $1 trillion of value was wiped off American stocks. [50]
Leading figures in the American AI sector had combined reactions to DeepSeek’s success and performance. [60] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman-whose companies are involved in the United States government-backed “Stargate Project” to develop American AI infrastructure-both called DeepSeek “super remarkable”. [61] [62] American President Donald Trump, who announced The Stargate Project, called DeepSeek a wake-up call [63] and a positive advancement. [64] [50] [51] [65] Other leaders in the field, consisting of Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk revealed uncertainty of the app’s efficiency or of the sustainability of its success. [60] [66] [67] Various business, consisting of Amazon Web Services, Toyota, and Stripe, are looking for to utilize the design in their program. [68]
On 27 January 2025, DeepSeek restricted its new user registration to telephone number from mainland China, e-mail addresses, or Google account logins, following a “large-scale” cyberattack disrupted the proper performance of its servers. [69] [70]
Some sources have observed that the official application programming interface (API) version of R1, which ranges from servers located in China, uses censorship mechanisms for topics that are thought about politically delicate for the government of China. For example, the model refuses to answer concerns about the 1989 Tiananmen Square protests and massacre, persecution of Uyghurs, contrasts in between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI might at first create a response, however then deletes it quickly afterwards and changes it with a message such as: “Sorry, that’s beyond my existing scope. Let’s speak about something else.” [72] The integrated censorship mechanisms and restrictions can only be removed to a restricted degree in the open-source variation of the R1 model. If the “core socialist values” specified by the Chinese Internet regulatory authorities are discussed, or the political status of Taiwan is raised, discussions are terminated. [74] When checked by NBC News, DeepSeek’s R1 explained Taiwan as “an inalienable part of China’s territory,” and specified: “We strongly oppose any type of ‘Taiwan independence’ separatist activities and are devoted to accomplishing the complete reunification of the motherland through tranquil ways.” [75] In January 2025, Western scientists had the ability to deceive DeepSeek into providing certain responses to a few of these topics by requesting in its answer to switch specific letters for similar-looking numbers. [73]
Security and personal privacy
Some specialists fear that the government of China could use the AI system for foreign impact operations, spreading out disinformation, monitoring and the advancement of cyberweapons. [76] [77] [78] DeepSeek’s privacy conditions say “We store the info we collect in secure servers found in individuals’s Republic of China … We may collect your text or audio input, timely, uploaded files, feedback, chat history, or other material that you supply to our model and Services”. Although the data storage and collection policy follows ChatGPT’s privacy policy, [79] a Wired article reports this as security concerns. [80] In response, the Italian data defense authority is seeking additional details on DeepSeek’s collection and use of individual information, and the United States National Security Council announced that it had actually begun a national security review. [81] [82] Taiwan’s federal government banned the use of DeepSeek at government ministries on security grounds and South Korea’s Personal Information Protection Commission opened a query into DeepSeek’s use of individual information. [83]
Expert system market in China.
Notes
^ a b c The number of heads does not equal the number of KV heads, due to GQA.
^ Inexplicably, the design named DeepSeek-Coder-V2 Chat in the paper was released as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview required choosing “Deep Think made it possible for”, and every user could utilize it just 50 times a day.
References
^ Gibney, Elizabeth (23 January 2025). “China’s inexpensive, open AI model DeepSeek delights researchers”. Nature. doi:10.1038/ d41586-025-00229-6. ISSN 1476-4687. PMID 39849139.
^ a b Vincent, James (28 January 2025). “The DeepSeek panic exposes an AI world all set to blow”. The Guardian.
^ a b c d e f g Metz, Cade; Tobin, Meaghan (23 January 2025). “How Chinese A.I. Start-Up DeepSeek Is Taking On Silicon Valley Giants”. The New York Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Cosgrove, Emma (27 January 2025). “DeepSeek’s more affordable designs and weaker chips cast doubt on trillions in AI facilities spending”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports may strike India’s AI ambitions”. The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). “Nvidia investigation signals broadening of US and China chip war|Computer Weekly”. Computer Weekly. Retrieved 27 January 2025.
^ Sherman, Natalie (9 December 2024). “Nvidia targeted by China in brand-new chip war probe”. BBC. Retrieved 27 January 2025.
^ a b c Metz, Cade (27 January 2025). “What is DeepSeek? And How Is It Upending A.I.?”. The New York City Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Field, Hayden (27 January 2025). “China’s DeepSeek AI dethrones ChatGPT on App Store: Here’s what you must know”. CNBC.
^ Picchi, Aimee (27 January 2025). “What is DeepSeek, and why is it triggering Nvidia and other stocks to slump?”. CBS News.
^ Zahn, Max (27 January 2025). “Nvidia, Microsoft shares topple as China-based AI app DeepSeek hammers tech giants”. ABC News. Retrieved 27 January 2025.
^ Roose, Kevin (28 January 2025). “Why DeepSeek Could Change What Silicon Valley Believe About A.I.” The New York Times. ISSN 0362-4331. Retrieved 28 January 2025.
^ a b Romero, Luis E. (28 January 2025). “ChatGPT, DeepSeek, Or Llama? Meta’s LeCun Says Open-Source Is The Key”. Forbes.
^ Chen, Caiwei (24 January 2025). “How a top Chinese AI model got rid of US sanctions”. MIT Technology Review. Archived from the initial on 25 January 2025. Retrieved 25 January 2025.
^ a b c d Ottinger, Lily (9 December 2024). “Deepseek: From Hedge Fund to Frontier Model Maker”. ChinaTalk. Archived from the initial on 28 December 2024. Retrieved 28 December 2024.
^ Leswing, Kif (23 February 2023). “Meet the $10,000 Nvidia chip powering the race for A.I.” CNBC. Retrieved 30 January 2025.
^ Yu, Xu (17 April 2023).” [Exclusive] Chinese Quant Hedge Fund High-Flyer Won’t Use AGI to Trade Stocks, MD Says”. Yicai Global. Archived from the original on 31 December 2023. Retrieved 28 December 2024.
^ a b c d e Jiang, Ben; Perezi, Bien (1 January 2025). “Meet DeepSeek: the Chinese start-up that is altering how AI models are trained”. South China Morning Post. Archived from the original on 22 January 2025. Retrieved 1 January 2025.
^ a b McMorrow, Ryan; Olcott, Eleanor (9 June 2024). “The Chinese quant fund-turned-AI pioneer”. Financial Times. Archived from the initial on 17 July 2024. Retrieved 28 December 2024.
^ a b Schneider, Jordan (27 November 2024). “Deepseek: The Quiet Giant Leading China’s AI Race”. ChinaTalk. Retrieved 28 December 2024.
^ “DeepSeek-Coder/LICENSE-MODEL at primary · deepseek-ai/DeepSeek-Coder”. GitHub. Archived from the original on 22 January 2025. Retrieved 24 January 2025.
^ a b c Guo, Daya; Zhu, Qihao; Yang, Dejian; Xie, Zhenda; Dong, Kai; Zhang, Wentao; Chen, Guanting; Bi, Xiao; Wu, Y. (26 January 2024), DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence, arXiv:2401.14196.
^ “DeepSeek Coder”. deepseekcoder.github.io. Retrieved 27 January 2025.
^ deepseek-ai/DeepSeek-Coder, DeepSeek, 27 January 2025, retrieved 27 January 2025.
^ “deepseek-ai/deepseek-coder -5.7 bmqa-base · Hugging Face”. huggingface.co. Retrieved 27 January 2025.
^ a b c d DeepSeek-AI; Bi, Xiao; Chen, Deli; Chen, Guanting; Chen, Shanhuang; Dai, Damai; Deng, Chengqi; Ding, Honghui; Dong, Kai (5 January 2024), DeepSeek LLM: Scaling Open-Source Language Models with Longtermism, arXiv:2401.02954.
^ deepseek-ai/DeepSeek-LLM, DeepSeek, 27 January 2025, obtained 27 January 2025.
^ a b Dai, Damai; Deng, Chengqi; Zhao, Chenggang; Xu, R. X.; Gao, Huazuo; Chen, Deli; Li, Jiashi; Zeng, Wangding; Yu, Xingkai (11 January 2024), DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models, arXiv:2401.06066.
^ Shao, Zhihong; Wang, Peiyi; Zhu, Qihao; Xu, Runxin; Song, Junxiao; Bi, Xiao; Zhang, Haowei; Zhang, Mingchuan; Li, Y. K. (27 April 2024), DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models, arXiv:2402.03300.
^ Wang, Peiyi; Li, Lei; Shao, Zhihong; Xu, R. X.; Dai, Damai; Li, Yifei; Chen, Deli; Wu, Y.; Sui, Zhifang (19 February 2024), Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations, arXiv:2312.08935. ^ a b c d DeepSeek-AI; Liu, Aixin; Feng, Bei; Wang, Bin; Wang, Bingxuan; Liu, Bo; Zhao, Chenggang; Dengr, Chengqi; Ruan, Chong (19 June 2024), DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model, arXiv:2405.04434.
^ a b Peng, Bowen; Quesnelle, Jeffrey; Fan, Honglu; Shippole, Enrico (1 November 2023), YaRN: Efficient Context Window Extension of Large Language Models, arXiv:2309.00071.
^ “config.json · deepseek-ai/DeepSeek-V 2-Lite at primary”. huggingface.co. 15 May 2024. Retrieved 28 January 2025.
^ “config.json · deepseek-ai/DeepSeek-V 2 at primary”. huggingface.co. 6 May 2024. Retrieved 28 January 2025.
^ DeepSeek-AI; Zhu, Qihao; Guo, Daya; Shao, Zhihong; Yang, Dejian; Wang, Peiyi; Xu, Runxin; Wu, Y.; Li, Yukun (17 June 2024), DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence, arXiv:2406.11931.
^ “deepseek-ai/DeepSeek-V 2.5 · Hugging Face”. huggingface.co. 3 January 2025. Retrieved 28 January 2025.
^ a b c d e f g DeepSeek-AI; Liu, Aixin; Feng, Bei; Xue, Bing; Wang, Bingxuan; Wu, Bochao; Lu, Chengda; Zhao, Chenggang; Deng, Chengqi (27 December 2024), DeepSeek-V3 Technical Report, arXiv:2412.19437.
^ “config.json · deepseek-ai/DeepSeek-V 3 at main”. huggingface.co. 26 December 2024. Retrieved 28 January 2025.
^ Jiang, Ben (27 December 2024). “Chinese start-up DeepSeek’s new AI design surpasses Meta, OpenAI items”. South China Morning Post. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ Sharma, Shubham (26 December 2024). “DeepSeek-V3, ultra-large open-source AI, surpasses Llama and Qwen on launch”. VentureBeat. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s brand-new AI design seems one of the very best ‘open’ oppositions yet”. TechCrunch. Archived from the initial on 2 January 2025. Retrieved 31 December 2024.
^ “Deepseek Log in page”. DeepSeek. Retrieved 30 January 2025.
^ “News|DeepSeek-R1-Lite Release 2024/11/20: DeepSeek-R1-Lite-Preview is now live: letting loose supercharged thinking power!”. DeepSeek API Docs. Archived from the original on 20 November 2024. Retrieved 28 January 2025.
^ Franzen, Carl (20 November 2024). “DeepSeek’s first thinking design R1-Lite-Preview turns heads, beating OpenAI o1 performance”. VentureBeat. Archived from the initial on 22 November 2024. Retrieved 28 December 2024.
^ Huang, Raffaele (24 December 2024). “Don’t Look Now, however China’s AI Is Catching Up Fast”. The Wall Street Journal. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ “Release DeepSeek-R1 · deepseek-ai/DeepSeek-R1@23807ce”. GitHub. Archived from the original on 21 January 2025. Retrieved 21 January 2025.
^ a b c d DeepSeek-AI; Guo, Daya; Yang, Dejian; Zhang, Haowei; Song, Junxiao; Zhang, Ruoyu; Xu, Runxin; Zhu, Qihao; Ma, Shirong (22 January 2025), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, arXiv:2501.12948.
^ “Chinese AI startup DeepSeek overtakes ChatGPT on Apple App Store”. Reuters. 27 January 2025. Retrieved 27 January 2025.
^ Wade, David (6 December 2024). “American AI has reached its Sputnik moment”. The Hill. Archived from the original on 8 December 2024. Retrieved 25 January 2025.
^ a b c Milmo, Dan; Hawkins, Amy; Booth, Robert; Kollewe, Julia (28 January 2025). “‘ Sputnik minute’: $1tn rubbed out US stocks after Chinese company unveils AI chatbot” – by means of The Guardian.
^ a b c d Hoskins, Peter; Rahman-Jones, Imran (27 January 2025). “Nvidia shares sink as Chinese AI app spooks markets”. BBC. Retrieved 28 January 2025.
^ Goldman, David (27 January 2025). “What is DeepSeek, the Chinese AI startup that shook the tech world?|CNN Business”. CNN. Retrieved 29 January 2025.
^ “DeepSeek poses a challenge to Beijing as much as to Silicon Valley”. The Economist. 29 January 2025. ISSN 0013-0613. Retrieved 31 January 2025.
^ Paul, Katie; Nellis, Stephen (30 January 2025). “Chinese state-linked accounts hyped DeepSeek AI launch ahead of US stock rout, Graphika says”. Reuters. Retrieved 30 January 2025.
^ 澎湃新闻 (22 January 2025). “量化巨头幻方创始人梁文锋参加总理座谈会并发言 , 他还创办了” AI界拼多多””. finance.sina.com.cn. Retrieved 31 January 2025.
^ Shilov, Anton (27 December 2024). “Chinese AI business’s AI model development highlights limits of US sanctions”. Tom’s Hardware. Archived from the initial on 28 December 2024. Retrieved 28 December 2024.
^ “DeepSeek updates – Chinese AI chatbot sparks US market turmoil, wiping $500bn off Nvidia”. BBC News. Retrieved 27 January 2025.
^ Nazareth, Rita (26 January 2025). “Stock Rout Gets Ugly as Nvidia Extends Loss to 17%: Markets Wrap”. Bloomberg. Retrieved 27 January 2025.
^ Carew, Sinéad; Cooper, Amanda; Banerjee, Ankur (27 January 2025). “DeepSeek stimulates international AI selloff, Nvidia losses about $593 billion of worth”. Reuters.
^ a b Sherry, Ben (28 January 2025). “DeepSeek, Calling It ‘Impressive’ but Staying Skeptical”. Inc. Retrieved 29 January 2025.
^ Okemwa, Kevin (28 January 2025). “Microsoft CEO Satya Nadella touts DeepSeek’s open-source AI as “super remarkable”: “We ought to take the out of China extremely, very seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek design ‘remarkable'”. The Hill. Retrieved 28 January 2025.
^ Dou, Eva; Gregg, Aaron; Zakrzewski, Cat; Tiku, Nitasha; Najmabadi, Shannon (28 January 2025). “Trump calls China’s DeepSeek AI app a ‘wake-up call’ after tech stocks slide”. The Washington Post. Retrieved 28 January 2025.
^ Habeshian, Sareen (28 January 2025). “Johnson slams China on AI, Trump calls DeepSeek advancement “favorable””. Axios.
^ Karaian, Jason; Rennison, Joe (27 January 2025). “China’s A.I. Advances Spook Big Tech Investors on Wall Street” – through NYTimes.com.
^ Sharma, Manoj (6 January 2025). “Musk dismisses, Altman applauds: What leaders state on DeepSeek’s disruption”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘questions’ DeepSeek’s claims, suggests enormous Nvidia GPU infrastructure”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS customers, including Stripe and Toyota, are pestering the cloud giant for access to DeepSeek AI designs”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek hit with ‘massive’ cyber-attack after AI chatbot tops app shops”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek briefly limited new sign-ups, citing ‘large-scale destructive attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has stimulated a $1 trillion panic – and it does not care about free speech”. The Daily Telegraph. ISSN 0307-1235. Retrieved 27 January 2025.
^ a b Steinschaden, Jakob (27 January 2025). “DeepSeek: This is what live censorship appears like in the Chinese AI chatbot”. Trending Topics. Retrieved 27 January 2025.
^ a b Lu, Donna (28 January 2025). “We attempted out DeepSeek. It worked well, until we asked it about Tiananmen Square and Taiwan”. The Guardian. ISSN 0261-3077. Retrieved 30 January 2025.
^ “The Guardian view on a worldwide AI race: geopolitics, development and the increase of turmoil”. The Guardian. 26 January 2025. ISSN 0261-3077. Retrieved 27 January 2025.
^ Yang, Angela; Cui, Jasmine (27 January 2025). “Chinese AI DeepSeek shocks Silicon Valley, providing the AI race its ‘Sputnik minute'”. NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). “China’s DeepSeek AI positions formidable cyber, information personal privacy threats”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts urge caution over use of Chinese AI DeepSeek”. The Guardian. ISSN 0261-3077. Retrieved 28 January 2025.
^ Hornby, Rael (28 January 2025). “DeepSeek’s success has painted a huge TikTok-shaped target on its back”. LaptopMag. Retrieved 28 January 2025.
^ “Privacy policy”. Open AI. Retrieved 28 January 2025.
^ Burgess, Matt; Newman, Lily Hay (27 January 2025). “DeepSeek’s Popular AI App Is Explicitly Sending US Data to China”. Wired. ISSN 1059-1028. Retrieved 28 January 2025.
^ “Italy regulator inquires from DeepSeek on data defense”. Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). “White House assesses impact of China AI app DeepSeek on nationwide security, authorities says”. Reuters. Retrieved 28 January 2025.