We found out several interesting things in our investigation. Here is a summary:

• From the video cards we analyzed, GeForce 8800 GT is the one that provides the best cost/benefit and best performance/kWh ratios for running Folding@Home. Of course you will get a higher score with a GeForce GTX 260 or GeForce GTX 280, but they are more expensive and also will consume more. If you think only about the points/kWh ratio (i.e. efficiency), then GeForce GTX 260 is the best: it produces more points per kWh consumed than all other video cards.
• A “weaker” video card won’t necessarily consume less power than a “stronger” one. Just see how GeForce 8800 GTS produces a lower score and consumes more than a GeForce 8800 GT.
• ATI video cards should not be used for running Folding@Home: they have a far lower points/kWh ratio compared to nVidia cards. A GeForce 8800 GT provides almost double the efficiency of a Radeon HD 4870. If you are building dedicated systems for running Folding@Home, stick with nVidia: you will get a higher score and a lower electricity bill.
• Very low-end video cards like Radeon HD 3450 and GeForce 8500 GT are not efficient to run Folding@Home and should be avoided. From the mainstream market GeForce 9500 GT was the one with the best performance and efficiency index (points/kWh), being our recommendation on this segment.
• Running the SMP client together with the GPU client won’t necessarily increase the system performance. In our tests we saw two out of three systems where performance decreased. We found out that for each video card running the GPU client Folding@Home will completely use one CPU core (on quad-core CPUs it will use one core per GPU, so with a quad-core CPU and two video cards, two CPU cores will be constantly used; on dual-core CPUs it will use one of the two cores all the time independently of the number of GPUs you have installed). So when you run the SMP client at the same time, both clients will compete for CPU utilization, leading to a lower performance. On the other hand, consumption also decreases.
• If you want to build a system for only running the SMP client, it is more efficient if you run it with a motherboard with on-board video, because the base consumption will be lower as you won’t have a video card installed.
• The Playstation 3 achieved one of the lowest points/kWh ratio, meaning that you will feel an increase on your electricity bill without a meaningful increase in your Folding@Home score. We see lots of people praising the math performance of PS3, but this performance isn’t converted in a huge Folding@Home score because each PS3 work unit doesn’t give a lot of points.
• If you are building a system to run Folding@Home, we think that the points/kWh should be your metric for efficiency. Buying a digital watt meter will help you a lot finding what you can change on your setup to have a more efficient system. From our experience you should keep systems with a points/kWh index of at least 1,000. Systems with indexes below that should be reevaluated.
• We tested only the parts we had available in our lab. If we didn’t include part A or B was because we didn’t have it. So please do not post comments like “why didn’t you tested part x?” – which actually mean “hey, can you please evaluate the PC I have at home for free?”. If you are really worried about consumption, buy a digital watt meter. It will also help you find out the consumption of other devices you have at home and see where you can save money.

In the text we explained how to find out the points/kWh index of a system, but here is a more practical summary:

• Measure the consumption with a digital watt meter connected to the system and with your Folding@Home client running. The results will be given in watts. Multiply it by 0.72 to have the results in kWh. This formula only works if you are running your system 24/7. If you are running it a different amount of time, the multiplying factor should be the number of hours divided by 1,000 (e.g. for 40 hours a month the multiplier is 0.04).
• Locate on your electricity bill the cost of each kWh. Multiply it by the result found about to have an estimate of how much it is costing you per month to keep that system running.
• Open the Log file of your Folding@Home client (FAHlog.txt), scroll down and see how much time it is taking to process each %. Convert the time found to seconds. Multiply this number by 100 to have the amount of time, in seconds, your client will take to process one work unit.
• Scroll down on the log file and locate the project number your client is running. Then locate this project at http://fah-web.stanford.edu/psummary.html and see how many points they will give you for processing a work unit from that project.
• Divide 86,400 by the time, in seconds, your client take to process one work unit. The result will be the number of work units your client can process per day. Multiply the result by the number of points they will give you for each work unit. You will have an estimate of number of points your system can generate per day.
• Multiply the result above by 30 to have the number of points you can generate per month. Divide this number by the monthly consumption from your system in kWh. The final result will be the points/kWh index. We recommend that you only keep systems delivering an index of at least 1,000.