Published On Jun 30, 2024
Some recent projections (e.g. by Leopold Aschenbrenner) have the size of AI training clusters growing by a factor of 10x every 2 years. The question is, is this actually feasible in terms of the amount of energy consumed by such clusters? Or will there be an energy bottleneck? While current clusters are large enough to be powered by for example a single nuclear power plant, they will quickly grow beyond that scale.
However, countries have a lot of experience at delivering electricity to a wide range of applications through the grid. The grid allows power sources to be decentralized and distributed, and the amount of power produced to be scaled up and down as needed. We discuss how, leveraging the grid, power for a 1 GW or even 10 GW or 100 GW cluster might be constructed.
Rather than nuclear power, we focus on discussing other energy sources, particularly solar. Solar panels can be produced and installed at scale in very little time, which will be necessary to construct such training clusters. There are other disadvantages here including requiring batteries to store power for nighttime or cloudy days, but when the math is finished, it is quite possible to provide power for these large-scale data centers. Watch the video to find out how.
#ai #energy #clusters
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0:00 Intro
0:21 Contents
0:28 Part 1: The energy constraint
0:54 Leopold Aschenbrenner's growth rate
1:36 Comparison with Moore's Law
2:03 Planning the gigawatt-scale data center
2:31 Creating special purpose data centers
3:13 Build data center next to power plant?
3:58 Nuclear power can't be built quickly
4:32 Is the push to green power an obstacle?
4:49 Why the electrical grid can help
6:03 Spiking power plants and existing sources
6:58 Constructing new energy sources
7:40 Existing increase in demand through electrification
8:47 Natural decreases in energy demand
9:27 Part 2: Distributed power generation
10:16 Why power distribution uses AC
11:10 Line loss calculations for data centers
12:26 International grid to decouple power generation and consumption
12:50 Example: Texas grid
13:36 Example: Canada selling power to the US
14:08 Types of power generation
14:16 Nuclear power: takes
14:36 Coal and natural gas: need fuels
14:48 Wind: not in my backyard
15:02 Hydroelectric power: need a river
15:17 Solar power: needs sun and land
15:47 Solar for data centers
16:21 What do different power amounts mean?
17:23 Part 3: Impact of green transformation
17:39 Companies build data centers to make money
18:24 AI companies have near unlimited capital
19:10 Example: Using solar is really fast
19:49 How to store electricity for night etc
20:40 Very high theoretical limits for storing energy
21:28 Pause GPUs when there's not enough power
21:56 One gigawatt data center feasibility
22:32 Cost of battery storage
23:20 Procuring solar panels
24:13 Is this price outrageous for power?
25:02 Adoption of green energy sources feasible
26:39 Conclusion
27:16 Investing in renewables
27:39 Outro