Actually AMD gpus are the most cost effective gpus and have supported full FP16/8 since 2 years. But people are locked in the Nvidia proprietary jail and no one seems to care... That is really sad and against the consummer interest, but also, deep learning will never become mainstream if it can only run on 5% of the world hardware (Nvidia).

> Actually AMD gpus are the most cost effective gpus.

No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus.

> But people are locked in the Nvidia proprietary jail and no one seems to care...

Sounds like you want to blame the users, but this is because Nvidia highly invested on GPGPU and Cuda since more than 10 years ago, while AMD did focus on something else like HSA. It is AMD’s fault.

I agree with kbumsik here. AMD only has themselves to blame. They have great hardware and fantastic theoretical benchmarks. Heck, even their SGEMMs are really fast and in line with the 15TFlops of FP32 on the VEGA 64s that we've benchmarked. However, it comes down to software ecosystem and optimizations for common deep learning subroutines.

MIOpen[1] is a step in this direction but still causes the VEGA 64 + MIOpen to be 60% of the performance of a 1080 Ti + CuDNN based on benchmarks we've conducted internally at Lambda. Let that soak in for a second: the VEGA 64 (15TFLOPS theoretical peak) is 0.6x of a 1080 Ti (11.3TFLOPS theoretical peak). MIOpen is very far behind CuDNN.

Lisa Su, if you're reading this, please give the ROCm team more budget!

1. https://github.com/ROCmSoftwarePlatform/MIOpen

Or, maybe, a new team.

> No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus.

Incorrect, see:

https://github.com/ROCmSoftwarePlatform/tensorflow-upstream

Do you know where we can find some benchmarks?

If AMD developed CUDA transpilers / DL backends / CUDNN equivalents people would switch over in a heartbeat. It's fully their fault for not investing in the software.

If they did that and had a card that got 2x performance / $ or more I would switch in a heartbeat.

AMD has a CUDA Transpiler (https://github.com/ROCm-Developer-Tools/HIP) and a multitude of other open source libraries through GPUOpen ( https://gpuopen.com/professional-compute/).

The quality and open source nature of their tools has resulted in much of my research group (real-time vision) increasingly and voluntarily moving to work on AMD platforms (we were previously almost exclusively using CUDA).

But the GPU needs to support ROCm, which is quite recent right? e.g. The MBP 2015's Radeon m370x doesn't seem to support it.

less recent than a 1080 or 2080ti

2080 Ti is brand new :/

Here is it: https://github.com/ROCm-Developer-Tools/HIP

Also, AMD does not limit FP performance on consumer cards.

> Also, AMD does not limit FP performance on consumer cards.

I don't understand this meme. The consumer cards are different chips with slow fp64 hardware. In what sense is that "limiting" performance relative to the enterprise cards?

On AMD, if I'm not not mistaken, FP16 is twice of FP32, as it should be

"For their consumer cards, NVIDIA has severely limited FP16 CUDA performance. GTX 1080’s FP16 instruction rate is 1/128th its FP32 instruction rate, or after you factor in vec2 packing, the resulting theoretical performance (in FLOPs) is 1/64th the FP32 rate, or about 138 GFLOPs."

https://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-...

"FP16 performance is 1/64th and FP64 is 1/32th of FP32 performance."

https://blog.inten.to/hardware-for-deep-learning-part-3-gpu-...

I agree with your broader point, but stating that Nvidia comprises a mere 5% of the world's hardware is disingenous in a post where you've only talked about GPUs. In actual fact, Nvidia has the overwhelming majority of the high end GPU market, especially in HPC and ML. And as much as I dislike the proprietary lock-in, they have by far the superior software ecosystem.

Actually nvidia isn't the high end leader if you account console APUs (almost all being from AMD). And most gpus in the world are from igpus, apus or ARM SoCs. Dgpu IS a small market, High end Dgpu is a few percents max.

Deep learning generally doesn't run on AMD GPUs, but it runs on a lot more than only Nvidia GPUs.

If we're only counting model training, it runs on CPUs, Google's TPUs, FPGAs, whatever other secret datacenter ASICs are out there, various DL-specific mobile chips, etc.

Way more than 5% of the world's hardware can run inference with deep neural nets, which is the important thing for mass adoption, and definitely more than only Nvidia GPUs can run training.

I haven't had a great experience using anything else than CUDA, e.g. OpenCL. Then again maybe I just lack the skills :x

Do you own AMD stock?

Why this question ? No I don't. My message is objective and openCL/sysCL allow to run on All accellerators, not just AMD nor Nvidia.

Because you started your comment with an objective falsehood, NVIDIA has the most cost effective GPUs as long as CUDA king and AMD doesn’t invest more on the software front

> 37% faster than the 1080 Ti with FP32, 62% faster with FP16, and 25% more expensive.

Maybe in theory, but you can get a new 1080Ti for $700 while 2080Ti is impossible to get under $1200, which makes it 70% more expensive. At that point 2x 1080Ti sounds way better than 1x 2080Ti for Deep Learning to me (up to 22TFlops and 22GB of RAM).

Hey bitL, thanks a lot for pointing this out. I think you hit the nail on the head in terms of the biggest weakness of our post: choosing the price. We've decided to make the spreadsheet that generated our graphs and (performance / $) tables public. You can view it here:

https://docs.google.com/spreadsheets/d/1La55B-AVHSv9LiQcs6GM...

You can copy that spreadsheet and insert whatever system price (in kilodollars) you want into B15:F15. Hope this makes everybody's decision making easier.

As a system builder and AI research company, we're trying to make benchmarks that are scientific, reproducible, correlate with real world training scenarios, and have accurate prices.

Are 2 1080TI better than a single 2080Ti?

Yes. 1080 Ti do not scale linearly, but in most of the benchmarks I’ve seen, two 1080 Ti are at least 1.75x faster than one when doing multi-GPU training.

1.75 / 1.36 (speed up of 2080 Ti over a single 1080 Ti) = 1.28. So expect 2x 1080 Ti to be about 30% faster.

You can see how multi-GPU training works with Titan V benchmarks in the link below. 1080 Ti have similar scaling profile.

https://deeptalk.lambdalabs.com/t/benchmarking-the-titan-v-v...

Thanks.. This is really helpful.

Sure thing :)

Not in theory, in practice, these are actual benchmarks. Your central argument is that prices within the first few weeks of a new product launch are higher than MSRP. That point will likely not hold true 30 days from now. It makes perfect sense to compare the products based on their MSRPs.

He's not talking about the benchmarks, he's talking about the prices - which as you pointed out are theoretical.

It's been a long time since GPU prices have been anywhere near MSRP, it seems dishonest to assume that they will be in the near future.

The 1080Ti is back at MSRP and the cryptocurrency demand has vanished, so what's left is the demand displaced by the high prices earlier.

In the past, it has been common that new hardware with low initial supply fetched premiums that lasted until supply met demand. It's not dishonest to assume that it's the same case here.

Exactly. We're sort of between a rock and a hard place in terms of choosing a price.

While we could decide to write some software that could pull today's market prices and update tables and graphs dynamically, we just decided to settle on a single number. If you are settling on a single number, the choice is either market price on date of publication or MSRP. Given that other GPUs tend towards their MSRP as time goes on, we decided to choose MSRP.

Your article still states that 2080ti is 25% more expensive, but lists 1080ti at $700 and 2080ti at $1,200 (which are correct prices from nvidia website) which is 70% more. Which one is right?

It states that the "typical GPU system" (estimated at ~1300$ without GPU) will be 25% more expensive.

By the way, the MSRP for a regular 2080Ti is 999$, the 1200$ is the Founder's Edition price.

MSRP may be $999, but you won’t find them for much below $1,200 for the time being.

GPU modules are manufactured in China. Their harmonized codes are covered in recently established tariffs. 10% tariffs are already hitting cards arriving at US ports. This tariff will increase to 25% on Jan 1.

Prices will stay well above MSRP.

You mean it MAY not hold true 30 days from now. Last year around this time the cryptocurrencies started spiking. Perhaps demand for graphics cards will go up again, and this time, maybe the demand for the 1080 Tis won't match the demand for the newer, shinier 2080 Tis.

Fair feedback, I've edited parent.

1080 Tis are back under MSRP right now and have been for some time. We decided to assume that 2080 Tis would exhibit similar behavior.

Unless crypto mining picks up again.

Note that these results are only for convnets. Has anyone posted RNN results?