DeepSeek’s R1 model training costs pour cold water on big tech’s massive AI spending

In mid-2024, Anthropic CEO Dario Amodei projected AI training costs to soar to such an extent that building a new model could cost upwards of $100 billion.

Amodei’s lofty claims appear to have been completely shot down with the publication of a new research paper from DeepSeek. In a recent paper, published in the academic journal Nature, the Chinese AI developer claims it spent a paltry amount training its flagship R1 model.

All told, training costs amounted to $294,000, with the company using 512 Nvidia H800 chips to build the model that had US companies sweating earlier this year.

It’s worth noting that these costs come in addition to around $6 million spent by the firm to create the base LLM R1 is built on. Regardless, the results are impressive given the far higher training costs associated with competing models.

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So what makes DeepSeek R1 tick?

Under the hood of DeepSeek R1

DeepSeek R1 is a ‘reasoning model', meaning it’s designed specifically to excel at tasks such as mathematics and coding. It’s also an ‘open weight’ model, so is freely available for anyone to download.

As ITPro reported earlier this year, the decision to offer R1 as an open weight model was welcomed by industry stakeholders, and also gave competitors a vital insight into how the model performed compared to others out there on the market.

R1 is among the most popular models currently available on AI community platform, Hugging Face, having been downloaded over 10 million times.

AI reasoning models are purposefully trained on real-world data, enabling them to “learn” how to solve specific problems. It’s a costly, long-winded process but has been a key focus for AI providers over the last 18-months as they offer users more intuitive tools.

According to this latest research paper, DeepSeek was able to reduce training costs through a carrot-and-stick type approach to reinforcement learning. Researchers essentially incentivized and rewarded the model to produce correct answers to user queries.

In a blog post dissecting the paper, researchers at Carnegie Mellon University noted that this was akin to a child playing video games, constantly learning new ways to progress.

“As the child navigates their avatar through the game world, they learn through trial and error that some actions (such as collecting gold coins) earn points, whereas others (such as running into enemies) set their score back to zero,” researchers explained.

“In a similar vein, DeepSeek-R1 was awarded a high score when it answered questions correctly and a low score when it gave wrong answers.”

So much for huge training costs

DeepSeek has previously hinted that its training processes were highly cost efficient. A preprint release of the study, published in January, pointed toward far lower costs compared to US competitors.

Some Silicon Valley figures questioned the veracity of DeepSeek’s claims at the time, but with R1 becoming the first peer-reviewed LLM, the company appears vindicated.

AI training has long been lamented as a costly, compute-intensive process for big tech providers. Amodei isn’t alone in highlighting the huge costs associated with this, either.

In 2023, OpenAI CEO Sam Altman hinted that foundation model training had cost upwards of $100 million.

These figures appear to have been confirmed during his appearance at a 2024 MIT event, where he said it’s “more than that”, per reports from Wired.

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