NVIDIA GeForce RTX 2060 Super Review Entry GPU Compute Leader


Power Tests

For our power testing, we used AIDA64 to stress the NVIDIA GeForce RTX 2060 Super, then HWiNFO to monitor power use and temperatures.


After the stress test has ramped up the NVIDIA GeForce RTX 2060 Super, we see it tops out at 179Watts under full load and 11Watts at idle. We were frankly surprised to see this result but it shows the power efficiency of NVIDIA’s cooling solution.

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 GeForce RTX 2060 SUPER which uses dual fans that exhaust hot air into the case and out the back.

NVIDIA RTX 2060 SUPER Temperatures
NVIDIA RTX 2060 SUPER Temperatures

Temperatures for the NVIDIA GeForce RTX 2060 SUPER run in a temperature range equal to other cards in this class.

Final Words

The big improvements in terms of compute, memory bandwidth, and memory capacity for the NVIDIA GeForce RTX 2060 Super has an enormous impact. Increasing memory size to 8GB had “Super-sized” impact. In some of our rendering tests and especially with Tensorflow training, we saw outsized gains over the 6GB GeForce RTX 2060. On some of these deep learning benchmarks, we could not run the RTX 2060 6GB cards because of memory constraints. With 8GB, the new NVIDIA GeForce RTX 2060 Super has significantly more deep learning training potential. If that is your intended CUDA application, we certainly think the $50 price premium for the Super part is worthwhile.

In the vast majority of tests, it is equal to if not slightly better than the previous generation GeForce RTX 2070 and GeForce GTX 1080 cards. For $399, one gets the latest generation GPU. On the AMD side, the new Navi based parts are unable to complete even half of our test suite due to the compute stack. NVIDIA, on the other hand, has a strong commitment to CUDA and GPU compute that scales all the way from this gaming marketed card up to NVIDIA Tesla V100 as we saw in our Inspur Systems NF5468M5 Review and beyond.

NVIDIA RTX 2060 SUPER Angle View
NVIDIA RTX 2060 SUPER Angle View

We think the NVIDIA GeForce RTX 2060 Super 8GB GPU is the perfect storm for price/ performance in the entry deep learning space. You get a big bang for your buck here. For our users, the extra $50 spent over the earlier GeForce RTX 2060 6GB is perhaps the best $50 spent in a system.


  1. Look at that TensorFlow training performance on pg. 5! That’s an insane upgrade over the Rtx 2060 6G. The Titan Rtxs change the chart’s scale but it’s a huge increase at the low end

  2. “A key reason that we started this series was to answer the cooling question.”
    If the answer to this is your statement about running equal to other cards, that would not seem to be either a strong endorsement nor conclusive.
    Hence I’m curious if it’d be possible to compare the 2060 Super vs. 2070 Blower. From your statement of test setup it appears the chart is single GPU, so perhaps multi-GPU would be closer to real-world. [yeah I know, ‘you guys should go buy 4 of those, a couple of these, some Titans …” etc., and I’ve spent $60k of your money that you don’t have 8]
    Remember to use some beefy case fans, or just state what’s being used.

  3. This review was very important to me, ended up demonstrating that the RTX 2060 SUPER is the true entry-level GPU for Machine Learning and Deep Learning. I have the following questions:
    Which manufacturer has the best quality plate, greater durability, greater strength to work for long time under full load?
    Is it possible to downclock this GPU by reducing its frequency? This is important in ML / DL processing that can last for days under full load, so the GPU does not constantly work overloaded, thereby increasing its lifespan and running the processing with more stability.
    Workstation motherboards, in case you have two video cards or an offboard video card and the integrated video processor, such as ASUS Pro WS X570-ACE, ASUS WS C246 PRO, Gigabyte C246-WU4, have in their bios some kind of control in which can I set a card for video driver and another GPU to leave only for ML / DL processing?
    Congratulations on the matter. I hope to briefly review the RTX 2070 SUPER.

  4. @asH
    It’s only tested in benchmarks where NVidia is faster, just remember how STH earns their money.

  5. I would love to know how we earn money @Misha? Sure is not from the STH publishing side for the last 10 years. Enlighten me. Seems like you know better than I do given the tone of that comment.

    @asH very simple reason. We do not have one to test. If we did, it would be in there.

  6. ” ..very simple reason. We do not have one to test. If we did, it would be in there.”
    -Then why wasnt that said that in the article?
    Wait! Are you then saying you ONLY test cards given to you by manufacturers??

  7. “On the AMD side, the new Navi based parts are unable to complete even half of our test suite due to the compute stack. ”

    …Then this statement is a half truth because on the AMD side their compute card, Vega VII (based in MI50/60), the 7nm(25%) big brother to Frontier wasnt tested because you didnt have one. ..Quite an impressive array of cards you did have. Just sayin’

  8. asH asH we tend to present the relevant data we have. For example, when we review a Xeon-based server we usually test with multiple SKUs. We do not test with all 50+ SKUs because it would take too long and we do not have every Xeon SKU.

    We buy cards to test from time-to-time. On the other hand, we knew the Radeon VII would have a *very* short lifespan in the market. Given the price tag, our budgets, and expected traffic from a Radeon VII review, we could not buy one. Radeon VII was a very short-lived product.

    On the Navi statement, that was based on AMD’s acknowledgment as well that their compute drivers for those cards were not ready yet. No half-truth there.

    If you would like to provide a Radeon VII, we are happy to test it just to add to the database. My sense is that you just want to see the now discountinued Radeon VII in these charts.

  9. I am surprised by the low DP FLOPS (FP64) performance for all of these. I know old GPUs had no or hardly any FP64 (like 1:24) but I thought newer cards had ratios like 1:3, or configurable allocations between FP32 and FP64, allowing up to 1:3 ratios. Is there some background to the testing that would clarify this, or are the advertised improvements in FP64 performance just hype?

  10. Dear all,

    I am really impressed with the benchmarks and your proffesionalism.
    Please do share how to achieve such a high result (6990) in superposition, would really like to know that, or at least to come somewhat close to that (currently breached the 6000 limit).
    Thank you in advance.

  11. Jason – William just adds what he gets from the output using the test configuration he has. If you have a different configuration, you will likely get something different. We do not have the resources to troubleshoot everyone’s specific configuration.


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