Most disruptive aspect of DeepSeek-R1
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Question 3 Not yet answered Marked out of 1.00 Which of the following is the MOST-disruptive aspect of the recent introduction of the DeepSeek-R1 model: Select one: a. DeepSeek-R1 extracted further efficiencies from use of existing generation GPUs with support for tensor cores b. DeepSeek-R1 cost less money and time to train, even though it performs almost on par with today's best LLMs c. DeepSeek-R1 recently emerged from an almost stealth mode even though efforts had been on going for at least 9 months d. DeepSeek-R1 demonstrated that the attention mechanism from the original transformer model could be improved upon to extract further efficiencies
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Hi Victor, let's explore why the DeepSeek-R1 model has been such a significant disruptor in the field of Artificial Intelligence.
Understanding DeepSeek-R1's Impact
To identify the most disruptive aspect, we need to look at what changed the standard expectations for developing high-performance Large Language Models.
Key Evaluation Criteria
- Performance: How does it rank?
- Efficiency: How much did it cost to train?
- Accessibility: Is it open or closed?
Let's evaluate the options. Option A mentions existing GPUs and tensor cores. While efficient, utilizing modern hardware is a standard practice for all high-end models, so this isn't the primary 'disruption'.
Option A: Technical optimization (Standard practice)
Now consider Option B. DeepSeek-R1 achieved performance comparable to the world's most powerful models, like G P T 4, but did so at a remarkably lower training cost—estimated at around 6 million dollars compared to the hundreds of millions spent by competitors.
The Economic Disruption
- Competitors: Cost $\approx$ Hundreds of Millions
- DeepSeek-R1: Cost $\approx$ $6 Million
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