What is the electric efficiency per watt for generative AI and computers?

Generative AI models are becoming increasingly complex and capable, but they can also be very energy-intensive to train and run. The current state-of-the-art in electric efficiency per watt for generative AI models is around 10-20%. This means that for every 10 watts of power used, the model can generate 1-2 watts of useful output.

There are a number of factors that contribute to the energy efficiency of a generative AI model. One important factor is the architecture of the model. Some architectures are more efficient than others, and researchers are constantly developing new architectures that are more energy-efficient.

Another important factor is the training data. The more data a model is trained on, the more energy it will consume. However, more data also leads to better results, so there is a trade-off between energy efficiency and accuracy.

Finally, the hardware used to train and run generative AI models also plays a role in energy efficiency. GPUs are more efficient than CPUs for training and running generative AI models, but they are also more expensive.

Here are some examples of recent advances in energy-efficient generative AI:

• In 2022, Google AI researchers developed a new training algorithm called “SparseML” that can reduce the energy consumption of generative AI models by up to 90%.
• In 2021, researchers at the University of Washington developed a new hardware architecture called “BrainChip” that is specifically designed for running generative AI models. BrainChip is significantly more energy-efficient than traditional GPUs.
• In 2020, researchers at OpenAI developed a new generative AI model called “GPT-Neo” that is more energy-efficient than previous models. GPT-Neo can generate text at a rate of 100 million words per second while consuming only 100 watts of power.

These are just a few examples of the recent advances in energy-efficient generative AI. As the field continues to develop, we can expect to see even more efficient models and hardware in the future.

In addition to generative AI models, computers are also becoming increasingly energy-efficient. The current state-of-the-art in electric efficiency per watt for computers is around 50-60%. This means that for every 10 watts of power used, the computer can generate 5-6 watts of useful output.

There are a number of factors that contribute to the energy efficiency of a computer. One important factor is the hardware. More efficient hardware consumes less power while still delivering the same performance. Another important factor is the software. Operating systems and applications can be optimized to consume less power.

Here are some examples of recent advances in energy-efficient computers:

• In 2022, Intel released the Alder Lake CPUs, which are up to 20% more energy-efficient than previous generations.
• In 2021, AMD released the Ryzen 6000 CPUs, which are also up to 20% more energy-efficient than previous generations.
• In 2020, Apple released the M1 chip, which is the most energy-efficient laptop chip on the market.

These are just a few examples of the recent advances in energy-efficient computers. As the field continues to develop, we can expect to see even more efficient computers in the future.

Overall, the current state-of-the-art in electric efficiency per watt for generative AI and computers is very good, but there is still room for improvement. Researchers are constantly developing new ways to make generative AI models and computers more energy-efficient. As these technologies continue to develop, we can expect to see them used in a wider range of applications, including those that require low power consumption.