Dual NVIDIA Titan RTX Power Consumption
For our power testing, we used AIDA64 to stress the NVIDIA Titan RTX’s, then HWiNFO to monitor power use and temperatures.
HWiNFO shows us both NVIDIA Titan RTX GPUs running; each Titan RTX will show us different power numbers depending on what each GPU is doing, the primary GPU is usually higher than the secondary GPU. We add the max power numbers of both Titan RTX’s together and then show the results in our graph.
We see the total for both Titan RTX GPUs at 633Watts under full load and 36Watts at idle. It is the most power hungry GPU setup we have tested in this series, yet far lower power than the multi-kW GPU servers we tested. We upgraded our PSU to a Thermaltake Toughpower DPS G RGB 1500W Titanium to be sure we would run clean power to the GPUs. Our bench platform is a dual socket Intel Xeon Scalable system with a large amount of RAM, we felt the need to upgrade the PSU. Keep in mind that for total power draw from the socket we saw numbers as high as 850watts!
A cool feature of the Toughpower DPS G is the monitoring interface which looks like this.
You can enter in your power costs at the far right corner, and it will keep you updated on the running power costs of your system. This is only a windows based PC feature though.
Additionally, with a mobile app, you can monitor the system on your phone.
For the IT administrator with several users running dual NVIDIA Titan RTX setups in an office setting, this type of capability may be useful. For the system integrators and VARs that read STH, this may be something to look into for these setups.
Dual NVIDIA Titan RTX Cooling Performance
A key reason that we started this series was to answer the cooling question. Blower-style coolers have different capabilities than some of the large dual and triple fan gaming cards. In the case of the NVIDIA Titan RTX which uses dual fans and huge full-card vapor chamber that spans the entire PCB which cools the GPU rather well. As we are using two Titan RTX’s in this review keeping spacing between the GPU’s is important, hence the reason we used the 4-Slot NVLink Bridge. The Titan RTX’s generate a lot of heat, especially when training so the extra space is important for air-flow.
Like the power tests, HWiNFO shows us both NVIDIA Titan RTX GPUs running; each Titan RTX will show us different temperature numbers depending on what each GPU is doing, the primary GPU is usually higher than the secondary GPU. We used the highest temperature for our graphs. Our warmest Titan RTX typically ran 10C higher than our single NVIDIA Titan RTX did.
We spent many days benchmarking the dual NVIDIA Titan RTX with NVLink system; it has had its ups and downs. We were a bit disappointed with some benchmarks not fully using these two GPU’s in NVLink. Frankly, that was expected.
Stepping back and looking at what users will be using these cards for, in workstations, dual NVIDIA Titan RTX GPUs with NVLink have a dramatic impact in some situations. For example, rendering performance was superb and that can have a real-world economic benefit. Offsetting the $5.1K plus tax initial purchase is the time saved in heavy render workloads.
Likewise, those innovators doing deep learning training on their workstations will benefit a great deal with the amount of memory and the raw compute power of the solution. Lower-end GPUs simply cannot run some deep learning workloads at reasonable batch sizes. In a workstation where one is constrained by physical machine size, lower-power wall circuits, and acoustics, there is a limit to how many GPUs one can run. Scaling up to two larger GPUs is very reasonable while getting the same performance from four or more GPUs may not be. Make no mistake, if you are a company hiring a deep learning engineer and want to show you are investing in them the dual NVIDIA Titan RTX with NVLink setup makes an excellent impression.