Deepseek, a newly launched AI model, is getting a lot of buzz recently and for all the obvious reasons. It’s believed to be extremely powerful and efficient. The level of its capabilities is unmatched by any of the models currently existing in the market.
Introduction.
We witnessed the massive growth and rise of ChatGPT back in 2022, when it reached a whopping 1 million users in just five days of its launch, proving what a huge impact AI can have on crowds. Deepseek AI claims to have overpowered the prominent ChatGPT and challenges its shortcomings. It even is, in no way, behind Google’s Gemini. So, why is Deepseek AI so efficient, and what’s all the hype around it? Is it the next big thing in technology? Let’s dig deeper!
Seeking Deepseek
“Mysterious force from the East”—this is what they call Deepseek in Silicon Valley, and it stands very true to it. Founded not very long ago, in 2023, the Artificial Intelligence firm aims to build and improve AI and make it omnipresent. They have been very consistent and promising with their AI model versions in the past few months.
It’s not something common you would find next door; it’s a breakthrough AI juggernaut designed to provide customized, contextually exact responses to each question thrown its way. All unique! Impressive, right?
Deepseek, based in Silicon Valley, is being heralded as the new and upcoming behemoth, leading China’s AI revolution with a discreet but strong presence. Even though they don’t have access to the cutting-edge American-made processors, this powerhouse AI firm boldly displays its own cutting-edge R1 model, which is said to have outperformed OpenAI’s o1 in numerous critical reasoning checks.
How is Deepseek Different?
Deepseek is very precise and solution-oriented. It handles text-based tasks and coding quite easily. It’s more focused on advanced multimodal AI and combines required images, texts, and other forms of data in its presented solution.
It customizes its solutions targeting specific sectors such as e-commerce and healthcare, ensuring adherence to local rules and meeting the unique demands of the Chinese market.
According to the Chinese company, the Nvidia H800 GPU allowed the AI model to be fully trained in 2.788 million hours, despite its size. A load-balancing strategy is also implemented into DeepSeek-V3’s design to reduce performance deterioration. This method was initially applied to its predecessor.
The AI model, basically, activates just the parameters related to the prompt’s topic, resulting in swift processing and more accuracy than normal models of this size. DeepSeek-V3 is pre-trained on 14.8 trillion tokens and generates high-quality replies using techniques like supervised fine-tuning and reinforcement learning.
According to Deepseek, the AI model uses the DualPipe algorithm, which has helped minimize training bottlenecks for cross-node expert parallelism. This enables the cluster to process 14.8 trillion tokens during pre-training with about zero communication overhead. To manage the traffic, Deepseek has restricted each token to at most four nodes. This ensures communication and computation go hand in hand.
The Size of Deepseek Model
Deepseek V3 is a Large Language Model (LLM). It has around 671 billion parameters! It’s really unlikely that one might get this extensive of a model in the open-source community.
With this, it has overtaken Meta’s Llama 3.1, which has around 405 billion parameters. Deepseek has trained its V3 model using 2,048 Nvidia H800 GPUs in just a couple of months, which on paper amounts to around 2.8 million GPU hours.
To give you an idea, Meta needed 30.8 million GPU hours to train Llama 3 over 54 days. Meta took approximately 11 times more compute power to train its Llama 3 with 405 billion parameters using a cluster containing 16,384 H100 GPUs!
The Chinese AI firm thus claims to have trained the AI model such that it can compete with the top models from heavyweights such as OpenAI, Meta, and Anthropic, but with an 11X reduction in GPU computation and hence cost. The claims have not been completely confirmed yet.
What Does the Model Lack?
The paper released by Deepseek reads:
“While acknowledging its strong performance and cost-effectiveness, we also recognize that DeepSeek-V3 has some limitations, especially on the deployment. Firstly, to ensure efficient inference, the recommended deployment unit for DeepSeek-V3 is relatively large, which might pose a burden for small-sized teams. Secondly, although our deployment strategy for DeepSeek-V3 has achieved an end-to-end generation speed of more than two times that of DeepSeek-V2, there still remains potential for further enhancement. Fortunately, these limitations are expected to be naturally addressed with the development of more advanced hardware.”
So, as is apparent, deployment is something concerning for Deepseek. Achieving a good deployment strategy requires advanced hardware, which may be difficult for small companies to obtain.
Deepseek’s model still lags behind advanced models like GPT-4o or o3 in terms of the number of parameters or reasoning capabilities. Despite that, the successes show that a sophisticated MoE language model may be trained with very few resources. This certainly necessitates a lot of optimizations and low-level programming, but the results look to be rather nice.
Chat GPT-4o vs DeepSeek-V3
So , in this hype by now you must be wondering how does Deepseek-V3 performs in comparison to ChatGPT-4o. Well worry not, we got you covered , here is a complete comparison between the two in tabular form.
Chat GPT-4o vs DeepSeek-V3 Features Comparison.
| Feature | GPT-4o | DeepSeek-V3 |
|---|---|---|
| Release Date | August 6, 2024 | December 27, 2024 |
| Knowledge Cut-off Date | October 2023 | Unknown |
| Open Source | No | Yes |
| Input Context Window | 128K tokens | 128K tokens |
| Maximum Output Tokens | 16.4K tokens | 8K tokens |
| Supported Modalities | Text, Image | Text |
| Input Cost per Million Tokens | $2.50 | $0.14 |
| Output Cost per Million Tokens | $10.00 | $0.28 |
| Model Parameters | Undisclosed | 671B (37B activated per token) |
| Training Tokens | Undisclosed | 14.8 Trillion |
| Training Compute | Undisclosed | 2.788M H800 GPU hours |
Chat GPT-4o vs DeepSeek-V3 -Model Performance
| Benchmark | GPT-4o | DeepSeek-V3 |
|---|---|---|
MMLU Massive Multitask Language Understanding – Tests knowledge across 57 subjects including mathematics, history, law, and more | ||
MMLU-Pro A more robust MMLU benchmark with harder, reasoning-focused questions, a larger choice set, and reduced prompt sensitivity | ||
MMMU Massive Multitask Multimodal Understanding – Tests understanding across text, images, audio, and video | Not available | |
HellaSwag A challenging sentence completion benchmark | Not available | |
HumanEval Evaluates code generation and problem-solving capabilities | ||
MATH Tests mathematical problem-solving abilities across various difficulty levels | ||
GPQA Tests PhD-level knowledge in chemistry, biology, and physics through multiple choice questions that require deep domain expertise | ||
IFEval Tests model’s ability to accurately follow explicit formatting instructions, generate appropriate outputs, and maintain consistent instruction adherence across different tasks | Not available |
TRENDING
Conclusion
Deepseek is a Chinese AI start-up, and it’s all over the news with the release of its futuristic large language model, DeepSeek V3. The model uses a humongous 671 billion parameters and has outperformed prominent AI models like Meta’s Llama 3.1 and OpenAI’s GPT-4o in benchmark tests in terms of text understanding, coding, and problem-solving. This achievement is a breakthrough for China’s AI industry.
Despite the lack of resources and amenities, the model still proves to be highly efficient. There are still some places, like deployment, where the model needs improvement, but overall, it is a big deal and deserves to be appreciated.
Breakthroughs like these make us wonder how amazing and magical the future of AI is!
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