Google says its new open-source AI model, Gemma 3, [1]achieves nearly the same performance as DeepSeek AI's R1 while using just one Nvidia H100 GPU, compared to an estimated 32 for R1. ZDNet reports:

> Using "Elo" scores, a common measurement system used to rank chess and athletes, Google claims Gemma 3 comes within 98% of the score of DeepSeek's R1, 1338 versus 1363 for R1. That means R1 is superior to Gemma 3. However, based on Google's estimate, the search giant claims that it would take 32 of Nvidia's mainstream "H100" GPU chips to achieve R1's score, whereas Gemma 3 uses only one H100 GPU.

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> Google's balance of compute and Elo score is a "sweet spot," the company claims. In a [2]blog post , Google bills the new program as "the most capable model you can run on a single GPU or TPU," referring to the company's custom AI chip, the "tensor processing unit." "Gemma 3 delivers state-of-the-art performance for its size, outperforming Llama-405B, DeepSeek-V3, and o3-mini in preliminary human preference evaluations on LMArena's leaderboard," the blog post relates, referring to the Elo scores. "This helps you to create engaging user experiences that can fit on a single GPU or TPU host."

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> Google's model also tops Meta's Llama 3's Elo score, which it estimates would require 16 GPUs. (Note that the numbers of H100 chips used by the competition are Google's estimate; DeepSeek AI has only disclosed an example of using 1,814 of Nvidia's less-powerful H800 GPUs to server answers with R1.) More detailed information is provided in a developer [3]blog post on HuggingFace, where the Gemma 3 repository is offered.



[1] https://www.zdnet.com/article/google-claims-gemma-3-reaches-98-of-deepseeks-accuracy-using-only-one-gpu/

[2] https://blog.google/technology/developers/gemma-3/

[3] https://huggingface.co/blog/gemma3