Whether you accept DeepSeek’s claims about how little it spent on training its incredible large language model at face value or not — it raises huge questions for the industry.
have captured the public imagination by producing fluent text in multiple languages in response to user prompts.they’ve invested to build ever more powerful models., has upset expectations about how much money is needed to build the latest and greatest AIs.I
Pretraining requires a lot of data and computing power. The companies collect data by crawling the web and scanning books. Computing is usually powered byWhy graphics? It turns out that both computer graphics and the artificial neural networks that underlie large language models rely on the same area of mathematics known as linear algebra.
Pretraining is, however, not enough to yield a consumer product like ChatGPT. A pretrained large language model is usually not good at following human instructions. Additionally, there are costs involved in data collection and computation in the instruction tuning and reinforcement learning from human feedback stages.. GPU training is a significant component of the total cost.
But then DeepSeek entered the fray and bucked this trend. DeepSeek sent shockwaves through the tech financial ecosystem., used a series of optimizations to make training cutting edge AI models significantly more economical.states that it took them less than $6 million dollars to train V3. They admit that this cost does not include costs of hiring the team, doing the research, trying out various ideas and data collection.
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