A Deep Dive into DeepSeek

Today, we are diving into the world of DeepSeek; with their models that boast significant developments for the entire AI industry.

The paradigm shifting release of DeepSeek’s R1 LLM sent waves through the tech and AI industry in January 2025. The Chinese start-up managed to develop a model that rival the likes of OpenAI’s GPT, using breakthrough methods of machine learning. This insight details their sweeping entrance into the AI landscape, and what it means for AI developers.

DeepSeek ‘the Disruptor’

FT described DeepSeek as ‘the disruptor’ – but why? Well, for AI solutions developers, DeepSeek represents a huge leap forward. Essentially, R1 is faster, cheaper, and can even out-perform Western counterparts.

Cost-efficient training

Making do with older and fewer Nvidia chips, DeepSeek researchers proved that necessity really is the mother of invention. They developed a series of more efficient techniques to create their models, like Reinforcement Learning and Reward Engineering. LLMs are built in two stages (pre-training and post-training), and then exhaustively refined. This refinement process usually takes a small army of human data labellers massive amounts of time, money, and labour. Reinforcement Learning (RL) is DeepSeek’s automation of this step, where the system is rewarded for generating the correct answer.

Additionally, using a Mixture-of-Experts approach, they significantly enhanced computational efficiency by only activating a small number of parameters at a time. This avoids redundant calculations, resulting in a 10x efficiency improvement compared to other leaders.

Altogether, DeepSeek have created new paradigms with their competitive model, created at fractions of the usual costs.

Open-Source development

Both DeepSeek’s R1, and their earlier V3, models are open-source. This means they are freely available for developers to access, modify, and deploy. In a market of increasing propriety, DeepSeek presents an attractive alternative. This move is creating a buzz for a new industry standard, putting pressure on other model developers to do the same.

The Democratisation of AI

So what does all this mean for AI developers?

Well, because of all their clever added efficiencies, DeepSeek claim to have significantly reduced the usually staggering LLM training costs. Figures for R1 are undisclosed, but their V3 model was based upon R1, and was apparently trained for $5.6 million. Whilst this sum does not include other necessary costs like GPUs and salaries, the billion dollar operations of competitors have had a rude awakening. Industry leaders are now scrambling to introduce similar efficiencies and build models for less cash.

Reducing costs by such huge sums will better democratise future AI implementation. It challenges the sweeping dominance that the few big, established players had. Budgets for AI projects will only continue to grow more realistic for smaller and mid-size companies who want to leverage AI in business ops.

A greener future

DeepSeek’s revelations provide fantastic news for our planet, too. The previous (and ginormous) estimations of AI energy demands have been entirely undermined. DeepSeek’s model was apparently trained using less than 10% of what was required for Meta’s Llama. Hopefully, this indicates that the total energy consumption of all the future AI development will be significantly lower.

Conclusion

DeepSeek is set to re-shape the AI industry. Offering high-quality, cost effective models, they have shocked the industry and intensified the AI arms race.

Comment below – we’d love to hear your thoughts on DeepSeek. Do you agree that the AI future lies in these smaller, cheaper models, or is there more to this conversation?

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