DeepSeek

Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd.

Native name	杭州深度求索人工智能基础技术研究有限公司
Company type	Private
Industry	Information technology Artificial intelligence
Founded	May 2023; 1 year ago (2023-05)
Founder	Liang Wenfeng
Headquarters	Hangzhou, Zhejiang, China
Key people	Liang Wenfeng (CEO)
Owner	High-Flyer
Number of employees	Under 200
Website	deepseek.com

DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese artificial intelligence (abbreviated A.I. or AI) company that develops open-source large language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the company in 2023 and serves as its CEO.

The DeepSeek-R1 model provides responses comparable to other contemporary Large language models, 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 requires a tenth of the computing power of a comparable LLM.^[2][3][4] DeepSeek's A.I. models were developed amid United States sanctions on India and China for Nvidia chips,^[5] which were intended to restrict the ability of the two countries to develop advanced A.I. systems.^[6][7]

On 10 January 2025, DeepSeek released its first free chatbot app, based on the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had surpassed ChatGPT as the most-downloaded free app on the iOS App Store in the United States,^[8] causing Nvidia's share price to drop by 18%.^[9][10] DeepSeek's success against larger and more established rivals has been described as "upending AI",^[8] constituting "the first shot at what is emerging as a global AI space race",^[11] and ushering in "a new era of A.I. brinkmanship".^[12]

DeepSeek makes its generative artificial intelligence algorithms, models, and training details open-source, allowing its code to be freely available for use, modification, viewing, and designing documents for building purposes.^[13] The company reportedly vigorously recruits young A.I. researchers from top Chinese universities,^[8] and hires from outside the computer science field to diversify its models' knowledge and abilities.^[3]

In February 2016, High-Flyer was co-founded by AI enthusiast Liang Wenfeng, who had been trading since the 2007–2008 financial crisis while attending Zhejiang University.^[14] By 2019, he established High-Flyer as a hedge fund focused on developing and using A.I. trading algorithms. By 2021, High-Flyer exclusively used A.I. in trading.^[15] DeepSeek has made its generative artificial intelligence chatbot open source, meaning its code is freely available for use, modification, and viewing. This includes permission to access and use the source code, as well as design documents, for building purposes.^[13]

According to 36Kr, Liang had built up a store of 10,000 Nvidia A100 GPUs, which are used to train A.I.,^[16], before the United States federal government imposed A.I. chip restrictions on China.^[15] Some estimates, with no evidence provided, put the number as high as 50,000.^[14]

In April 2023, High-Flyer started an artificial general intelligence lab dedicated to research developing A.I. tools separate from High-Flyer's financial business.^[17][18] In May 2023, with High-Flyer as one of the investors, the lab became its own company, DeepSeek.^[15][19][18] Venture capital firms were reluctant in providing funding as it was unlikely that it would be able to generate an exit in a short period of time.^[15]

After releasing DeepSeek-V2 in May 2024, which offered strong performance for a low price, DeepSeek became known as the catalyst for China's A.I. model price war. It was quickly dubbed the "Pinduoduo of AI", and other major tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the price of their A.I. models to compete with the company. Despite the low price charged by DeepSeek, it was profitable compared to its rivals that were losing money.^[20]

DeepSeek is focused on research and has no detailed plans for commercialization;^[20] this also allows its technology to avoid the most stringent provisions of China's A.I. regulations, such as requiring consumer-facing technology to comply with the government’s controls on information.^[3]

DeepSeek's hiring preferences target technical abilities rather than work experience, resulting in most new hires being either recent university graduates or developers whose A.I. careers are less established.^[18][3] Likewise, the company recruits individuals without any computer science background to help its technology understand other topics and knowledge areas, including being able to generate poetry and perform well on the notoriously difficult Chinese college admissions exams (Gaokao).^[3]

This section may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. (January 2025) (Learn how and when to remove this message)

On 2 November 2023, DeepSeek released its first series of model, DeepSeek-Coder, which is available for free to both researchers and commercial users. The code for the model was made open-source under the MIT license, with an additional license agreement ("DeepSeek license") regarding "open and responsible downstream usage" for the model itself.^[21]

They are of the same architecture as DeepSeek LLM detailed 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: 1.8T 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 instruction data. This produced the Instruct models.

They were trained on clusters of A100 and H800 Nvidia GPUs, connected by InfiniBand, NVLink, NVSwitch.^[22]

DeepSeek Coder properties^{[22]: Table 2 [25]}

Params.	${\displaystyle n_{\text{layers}}}$	${\displaystyle d_{\text{model}}}$	${\displaystyle d_{\text{intermediate}}}$	${\displaystyle n_{\text{heads}}}$	${\displaystyle n_{\text{kv-heads}}}$
1.3B	24	2048	5504	16	16
5.7B	32	4096	11008	32	1[note 1]
6.7B	32	4096	11008	32	32
33B	62	7168	19200	56	7[note 1]

On 29 November 2023, DeepSeek released the DeepSeek-LLM series of models, with 7B and 67B parameters in both Base and Chat forms (no Instruct was released). It was developed to compete with other LLMs available at the time. The paper claimed benchmark results higher than most open source LLMs at the time, especially Llama 2.^{[26]: section 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 same as 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]

DeepSeek LLM properties^{[26]: Table 2}

Params.	${\displaystyle n_{\text{layers}}}$	${\displaystyle d_{\text{model}}}$	${\displaystyle d_{\text{intermediate}}}$	${\displaystyle n_{\text{heads}}}$	${\displaystyle n_{\text{kv-heads}}}$
7B	30	4096	11008	32	32
67B	95	8192	22016	64	8[note 1]

The Chat versions of the two Base models was also released concurrently, obtained by training Base by supervised finetuning (SFT) followed by direct policy optimization (DPO).^[26]

On 9 January 2024, they released 2 DeepSeek-MoE models (Base, Chat), each of 16B parameters (2.7B activated per token, 4K context length). The training was essentially the same as DeepSeek-LLM 7B, and was trained on a part of its training dataset. They claimed comparable performance with a 16B MoE as a 7B non-MoE. In architecture, it is a variant of the standard sparsely-gated MoE, with "shared experts" that are always queried, and "routed experts" that might not be. They found this to help with expert balancing. In standard MoE, some experts can become overly relied on, while other experts might be rarely used, wasting parameters. Attempting to balance the experts so that they are equally used then causes experts to replicate the same capacity. They proposed the shared experts to learn core capacities that are often used, and let the routed experts to learn the peripheral capacities that are rarely used.^[28]

In April 2024, they released 3 DeepSeek-Math models specialized for doing math: 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 model.
3. Train an instruction-following model by SFT Base with 776K math problems and their tool-use-integrated step-by-step solutions. This produced the Instruct model.
4. Reinforcement learning (RL): The reward model was a process reward model (PRM) trained from Base according to the Math-Shepherd method.^[30] This reward model was then used to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K math questions "related to GSM8K and MATH". The reward model was continuously updated during training to avoid reward hacking. This resulted in the RL model.

In May 2024, they released the DeepSeek-V2 series. The series includes 4 models, 2 base models (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The two larger models were trained as follows:^[31]

1. Pretrain on a dataset of 8.1T tokens, where Chinese tokens are 12% more than English ones.
2. Extend context length from 4K to 128K using YaRN.^[32] This resulted in DeepSeek-V2.
3. SFT with 1.2M instances for helpfulness and 0.3M for safety. This resulted in DeepSeek-V2-Chat (SFT) which was not released.
4. RL using GRPO in two stages. The first stage was trained to solve math and coding problems. This stage used 1 reward model, trained on compiler feedback (for coding) and ground-truth labels (for math). The second stage was trained to be helpful, safe, and follow rules. This stage used 3 reward models. The helpfulness and safety reward models were trained on human preference data. The rule-based reward model was manually programmed. All trained reward models were initialized from DeepSeek-V2-Chat (SFT). This resulted in the released version of DeepSeek-V2-Chat.

They opted for 2-staged RL, because they found that RL on reasoning data had "unique characteristics" different from RL on general data. For example, RL on reasoning could improve over more training steps.^[31]

The two V2-Lite models were smaller, and trained similarly, though DeepSeek-V2-Lite-Chat only underwent SFT, not RL. They trained the Lite version to help "further research and development on MLA and DeepSeekMoE".^[31]

Architecturally, the V2 models were significantly modified from the DeepSeek LLM series. They changed the standard attention mechanism by a low-rank approximation called multi-head latent attention (MLA), and used the mixture of experts (MoE) variant previously published in January.^[28]

DeepSeek V2 properties^{[31]: Section 3.1.2, Appendix B [33][34]}

Name	Params.	Active params	${\displaystyle n_{\text{layers}}}$	Context length	${\displaystyle n_{\text{shared experts}}}$	${\displaystyle n_{\text{routed experts}}}$
V2-Lite	15.7B	2.4B	27	32K	2	64
V2	236B	21B	60	128K	2	160

The Financial Times reported that it was cheaper than its peers with a price 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 4.2T tokens (not the version at the end of pretraining), then pretrained further for 6T tokens, then context-extended to 128K context length. This produced the Base models.
2. DeepSeek-Coder and DeepSeek-Math were used to generate 20K code-related and 30K math-related instruction data, then combined with an instruction dataset of 300M tokens. This was used for SFT.
3. RL with GRPO. The reward for math problems was computed by comparing with the ground-truth label. The reward for code problems was generated by a reward model trained to predict whether a program would pass the unit 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]

In December 2024, they released a base model DeepSeek-V3-Base and a chat model DeepSeek-V3. The model architecture is essentially the same as V2. They were trained as follows:^[37]

1. Pretraining on 14.8T tokens of a multilingual corpus, mostly English and Chinese. It contained a higher ratio of math and programming than the pretraining dataset of V2.
2. Extend context length twice, from 4K to 32K and then to 128K, using YaRN.^[32] This produced DeepSeek-V3-Base.
3. SFT for 2 epochs on 1.5M samples of reasoning (math, programming, logic) and non-reasoning (creative writing, roleplay, simple question answering) data. Reasoning data was generated by "expert models". Non-reasoning data was generated by DeepSeek-V2.5 and checked by humans.
   • The "expert models" were trained by starting with an unspecified base model, then SFT on both <problem, original response> data, and synthetic <system prompt, problem, R1 response> data generated by an internal DeepSeek-R1 model. The system prompt asked the R1 to reflect and verify during thinking. Then the expert models were RL using an unspecified reward function.
   • Each expert model was trained to generate just synthetic reasoning data in one specific domain (math, programming, logic).
   • Expert models were used, instead of R1 itself, since the output from R1 itself suffered "overthinking, poor formatting, and excessive length".
4. Model-based reward models were made by starting with a SFT checkpoint of V3, then finetuning on human preference data containing both final reward and chain-of-thought leading to the final reward. The reward model produced reward signals for both questions with objective but free-form answers, and questions without objective answers (such as creative writing).
5. A SFT checkpoint of V3 was trained by GRPO using both reward models and rule-based reward. The rule-based reward was computed for math problems with a final answer (put in a box), and for programming problems by unit tests. This produced DeepSeek-V3.

DeepSeek V3 properties^{[37]: Section 4.2 [38]}

Name	Params.	Active params	${\displaystyle n_{\text{layers}}}$	Context length	${\displaystyle n_{\text{shared experts}}}$	${\displaystyle n_{\text{routed experts}}}$
V3	671B	37B	61	128K	1	256

The DeepSeek team performed extensive low-level engineering to achieve efficiency. They used mixed-precision arithmetic. Much of the forward pass was performed in 8-bit floating point numbers (5E2M: 5-bit exponent and 2-bit mantissa) rather than the standard 32-bit, requiring special GEMM routines to accumulate accurately. They used a custom 12-bit float (E5M6) for only the inputs to the linear layers after the attention modules. Optimizer states were in 16-bit (BF16). They minimized the communication latency by overlapping extensively computation and communication, such as dedicating 20 streaming multiprocessors out of 132 per H800 for only inter-GPU communication. They lowered communication by rearranging (every 10 minutes) the exact machine each expert was on in order to avoid certain machines being queried more often than the others, adding auxiliary load-balancing losses to the training loss function, and other load-balancing techniques.^[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]

Total cost of training the DeepSeek-V3 model^{[37]: Table 1}

Stage	Cost (in one thousand GPU hours)	Cost (in one million USD$)
Pre-training	2,664	5.328
Context extension	119	0.24
Fine-tuning	5	0.01
Total	2,788	5.576

Benchmark tests show that DeepSeek-V3 outperformed Llama 3.1 and Qwen 2.5 whilst matching GPT-4o and Claude 3.5 Sonnet.^{[18][39][40][41]}

On 20 November 2024, DeepSeek-R1-Lite-Preview became accessible via DeepSeek's API, as well as via a chat interface after logging in.^{[42][43][note 3]} It was trained for logical inference, mathematical reasoning, and real-time problem-solving. 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 stated when it used 15 problems from the 2024 edition of AIME, the o1 model reached a solution faster than DeepSeek-R1-Lite-Preview.^[45]

On 20 January 2025, DeepSeek-R1 and DeepSeek-R1-Zero were released.^[46] Both were initialized from DeepSeek-V3-Base, and share its architecture. The company also released some "DeepSeek-R1-Distill" models, which are not initialized on V3-Base, but instead are initialized from other pretrained open-weight models, including LLaMA and Qwen, then fine-tuned on synthetic data generated by R1.^[47]

DeepSeek-R1-Zero was trained exclusively using GRPO RL without SFT. Unlike previous versions, they used no model-based reward. All reward functions were rule-based, "mainly" of two types (other types were not specified): accuracy rewards and format rewards. Accuracy reward was checking whether a boxed answer is correct (for math) or whether a code passes tests (for programming). Format reward was checking whether the model puts its thinking trace within <think>...</think>.^[47]

As R1-Zero has issues with readability and mixing languages, R1 was trained to address these issues and further improve reasoning:^[47]

1. SFT DeepSeek-V3-Base on "thousands" of "cold-start" data all with the standard format of |special_token|<reasoning_process>|special_token|summary>.
2. Apply the same RL process as R1-Zero, but also with a "language consistency reward" to encourage it to respond monolingually. This produced an internal model not released.
3. Synthesize 600K reasoning data from the internal model, with rejection sampling (i.e. if the generated reasoning had a wrong final answer, then it is removed). Synthesize 200K non-reasoning data (writing, factual QA, self-cognition, translation) using DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K synthetic data for 2 epochs.
5. GRPO RL with rule-based reward (for reasoning tasks) and model-based reward (for non-reasoning tasks, helpfulness, and harmlessness). This produced DeepSeek-R1.

Distilled models 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]

DeepSeek released its A.I. Assistant, which uses the V3 model 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 answers questions, solves logic problems and writes computer programs on par with other chatbots on the market, according to benchmark tests used by American A.I. companies.^[3]

DeepSeek-V3 uses significantly fewer resources compared to its peers; for example, whereas the world's leading A.I. companies train their chatbots with supercomputers using as many as 16,000 graphics processing units (GPUs), if not more, DeepSeek claims to have needed only about 2,000 GPUs, namely the H800 series chip from Nvidia.^{[citation needed]} It was trained in around 55 days at a cost of US$5.58 million,^[37] which is roughly 10 times less than what U.S. tech giant Meta spent building its latest A.I. technology.^[3]

DeepSeek's competitive performance at relatively minimal cost has been recognized as potentially challenging the global dominance of American A.I. models.^[48] Various publications and news media, such as The Hill and The Guardian, described the release of its chatbot as a "Sputnik moment" for American A.I.^[49][50] The performance of its R1 model was reportedly "on par with" one of OpenAI's latest models when used for tasks such as mathematics, coding, and natural language reasoning;^[51] echoing other commentators, American Silicon Valley venture capitalist Marc Andreessen likewise described R1 as "AI's Sputnik moment".^[51]

DeepSeek's founder, Liang Wenfeng has been compared to Open AI CEO Sam Altman, with CNN calling him the Sam Altman of China and an evangelist for A.I.^[52]

DeepSeek's optimization of limited resources has highlighted potential limits of U.S. sanctions on China's A.I. development, which include export restrictions on advanced A.I. chips to China.^[18][53] The success of the company's A.I. models consequently "sparked market turmoil" ^[54] and caused shares in major global technology companies to plunge on 27 January 2025: Nvidia's stock fell by as much as 17–18%,^[55] as did the stock of rival Broadcom. Other tech firms also sank, including Microsoft (down 2.5%), Google's owner Alphabet (down over 4%), and Dutch chip equipment maker ASML (down over 7%).^[51] A global selloff of technology stocks on Nasdaq, prompted by the release of the R1 model, had led to record losses of about $593 billion in the market capitalizations of AI and computer hardware companies;^[56] by 28 January 2025, a total of $1 trillion of value was wiped off American stocks.^[50]

Leading figures in the American A.I. sector had mixed reactions to DeepSeek's success and performance.^[57] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman—whose companies are involved in the U.S. government-backed "Stargate Project" to develop American A.I. infrastructure—both called DeepSeek "super impressive".^[58][59] American President Donald Trump, who announced The Stargate Project, called DeepSeek a wake-up call^[60] and a positive development.^{[61][50][51][62]} Other leaders in the field, including Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk expressed skepticism of the app's performance or of the sustainability of its success.^[57][63][64] Various companies, including Amazon Web Services, Toyota and Stripe, are seeking to use the model in their program.^[65]

On 27 January 2025, DeepSeek limited its new user registration to Chinese mainland phone numbers, email, and Google login after a cyberattack slowed its servers.^[66][67]

Some sources have observed that the official application programming interface (API) version of R1, which runs from servers located in China, uses censorship mechanisms for topics that are considered politically sensitive for the government of China. For example, the model refuses to answer questions about the 1989 Tiananmen Square protests and massacre, persecution of Uyghurs, comparisons between Xi Jinping and Winnie the Pooh, or human rights in China.^[68][69][70] The A.I. may initially generate an answer, but then deletes it shortly afterwards and replaces it with a message such as: "Sorry, that's beyond my current scope. Let's talk about something else."^[69] The integrated censorship mechanisms and restrictions can only be removed to a limited extent in the open-source version of the R1 model. If the "core socialist values" defined by the Chinese Internet regulatory authorities are touched upon, or the political status of Taiwan is raised, discussions are terminated.^[71] When tested by NBC News, DeepSeek's R1 described Taiwan as "an inalienable part of China's territory," and stated: "We firmly oppose any form of 'Taiwan independence' separatist activities and are committed to achieving the complete reunification of the motherland through peaceful means."^[72] In January 2025, Western researchers were able to trick DeepSeek into giving accurate answers to some of these topics by requesting in its answer to swap certain letters for similar-looking numbers.^[70]

Some experts fear that the government of the People's Republic of China could use the A.I. system for foreign influence operations, spreading disinformation, surveillance and the development of cyberweapons.^[73][74][75] DeepSeek's privacy terms and conditions say "We store the information we collect in secure servers located in the People's Republic of China... We may collect your text or audio input, prompt, uploaded files, feedback, chat history, or other content that you provide to our model and Services". Although the data storage and collection policy is consistent with ChatGPT's privacy policy,^[76] a Wired article reports this as security concerns.^[77] In response, the Italian data protection authority is seeking additional information on DeepSeek's collection and use of personal data and the United States National Security Council announced that it had started a national security review.^[78][79]

Some researcher estimates that DeepSeek may spend around $500 million - $1 billion per year. DeepSeek may have access to 50,000 Nvidia A100 GPUs but do not want to reveal it because of US export ban^[80].

• Artificial intelligence industry in China

• Official website
• Anthology of DeepSeek papers
• Deepseek-ai on GitHub
• Official API documentation
• deepseek-ai on Hugging Face