
Nvidia Announces “Titan V” Video Card: GV100 for $3000 - yread
https://www.anandtech.com/show/12135/nvidia-announces-nvidia-titan-v-video-card-gv100-for-3000-dollars
======
bryanlarsen
This is targeted at ML, which means this is targeted at Linux. Which means us
system administrators have to deal with the piece of shit that is the Nvidia
binary driver.

\- it doesn't work with Wayland

\- it doesn't work in UEFI mode with CSM enabled. There are motherboards
available that won't boot in UEFI mode with CSM disabled without a monitor
attached.

\- it doesn't work with Secure Boot

\- about half of their releases fail to work with at least one of the pieces
of software we try to use it with. The current release 384.90-0ubuntu0.16.04.2
in particular crashes our software.

And for some reason Nvidia has the reputation for being good at drivers.
FirePro was also really bad, but I haven't had to deal with that for years
now...

~~~
jamesfmilne
The NVIDIA driver is most certainly not a piece of shit.

NVIDIA is a bit behind on things like Wayland, but it has been way ahead of
AMD for the last 10+ years on Linux for people running mission-critical
software that requires good OpenGL drivers.

Try running with AMD hardware on RHEL/CentOS 6/7 (the OS many major VFX
facilities use), you'll have a Bad Time (tm).

I am speaking as someone who has been maintaining software & hardware running
Linux making heavy use of NVIDIA hardware for the last ten years.

The NVIDIA driver on macOS on the other hand, now there is a lamentable
situation...

~~~
winter_blue
What about the open-source drivers? The state of Nouveau (open source Nvidia
drivers) is pretty terrible[1], right?

The proprietery AMD Catalyst™ (fglrx) drivers are known to be bad[2], but
AMDGPU[3] is supposed to be the best, when it comes to open source GPU driver
support on Linux, based on what I've read.

Better than Nouveau or Nvidia's proprietery blobs. Don't people sometimes buy
AMD cards just for its excellent native/open-source driver support?

[1]
[https://www.phoronix.com/scan.php?page=article&item=nouveau-...](https://www.phoronix.com/scan.php?page=article&item=nouveau-410-blob&num=1)

[2]
[https://www.phoronix.com/scan.php?page=article&item=openclos...](https://www.phoronix.com/scan.php?page=article&item=openclose-
amdnv-2017&num=1)

[3] [https://help.ubuntu.com/community/AMDGPU-
Driver](https://help.ubuntu.com/community/AMDGPU-Driver) |
[https://help.ubuntu.com/community/RadeonDriver](https://help.ubuntu.com/community/RadeonDriver)

~~~
jamesfmilne
I'm not arguing that AMD's new open-source drivers appear to be pretty good,
but you can't use them on current RHEL 7 since the RHEL kernel is frozen on
the 3.10.x line, which locks out a lot of people from using AMD GPU hardware
on Linux.

~~~
nbanks
It is possible to compile your own Kernel in Red Hat. You may loose some
customer support, but at least everything will be familiar.

------
modeless
Ugh, this company desperately needs some competition to keep prices in check.
AMD really has no one to blame but themselves for their lack of success in ML.
They could have been a real competitor years ago with a relatively small
investment in software but they didn't even start seriously trying to compete
with CuDNN until this year.

Edit: I didn't originally see that the die size of this chip is nearly double
that of the older Titan XP. Perhaps the price isn't as outrageous as I first
thought! I'm actually glad that there's a big enough market now to support
this kind of ultra high end product. It's certainly a beast and I want one.

~~~
TheArcane
> needs some competition to keep prices in check.

Prices as well as the tech itself. $3000 and still just 12GB. I wonder why
this isn't in the range of ~64/128GB yet... too expensive for the type of
memory?

~~~
sliken
High bandwidth GDDR5 is more expensive than DDR4. HBM is more expensive again.
On top of that the high bandwidth chips are lower density, and the complexity
of higher bandwidth links gets more complicated as you get more chips
involved.

Low end servers use normal dimm, but typically not many dimms per channel,
typically 1-3. But to hit the higher memory configurations you end up putting
buffer chips on the dimms which makes them more expensive and also slightly
higher latency.

So 500GB/sec to 128GB ram using HBM2 would be difficult, expensive, and likely
wouldn't fit in today's GPU form factors... yet.

GPUs generally seem to be mostly hungry for more bandwidth and not that many
cases (percentage wise) is 16GB a major limitation. However in those cases
check out the power9, the nvlink that x86's uses for GPU<->GPU communication
can be used for CPU <-> GPU communication and avoid the high latency/low
bandwidth pci-e links.

------
c1505
I don't get the price complaints. This is targeted for deep learning and looks
to be similar in performance to a card that currently costs 3x the amount.
This is a huge price cut for the compute performance and should make cloud GPU
time cheaper and/or more efficient.

~~~
bllguo
I'd hazard to guess that most people complaining about price are misguided
gamers who think Nvidia is now charging them 5x more for a card.

------
buildbot
Somewhat surprising they didn't release this card as a Quadro GPU, given its
high price tag even by Titan standards. Still for single GPU machine learning,
this is almost exactly a V100 unless you need the extra 4GB of memory.

You could also buy ~4 1080Ti cards, but unless you can't use the FP16 tensor
cores, you'd still lose heavily to a single V100.

~~~
Namidairo
Between the 4GB of memory (+wider memory bus) and the driver allowing you to
use it in a hypervisor, you are paying a $5000 premium for the V100.

A price premium that will really stack up when certain cloud providers want to
buy some of these in bulk.

Edit: The *999 pricing confused me, it's actually $5000 more.

------
huffpopo
I do deep learning for a day job, this card is for me. Going on the specs
it'll be worth it upgrading my 1080 TIs but I'll wait for a proper review
before committing $12K.

------
sabalaba
Wanted to note that Lambda Labs now offers Titan Vs on our Lambda Quad
workstations. They're available here:
[https://lambdal.com/products/quad](https://lambdal.com/products/quad).

We're excited to see Volta come down below $5,000 / GPU. We're also looking
forward to AMD, Intel, Graphcore, Rex, Bitmain, Vathys.ai, Wave Computing,
Tesla or Cerebras providing competitive alternatives.

~~~
ryanlol
What's up with the hidden $857 "Labor/Shipping/Handling" charge? It seems a
little out of place given that you guys already seem to be charging _huge_
premiums on all the hardware.

This all seems really expensive compared to the local PC shops that seem to
offer most of the components for less, while only charging ~100 euros for
assembly and testing.

Do you guys have some really fancy cooling solution that you forgot to mention
on the site?

~~~
analognoise
I think you were looking for "outrageously overpriced".

~~~
ryanlol
Pretty much, yeah. Though I'm still curious if getting your computers "Built
by Researchers" has some novel advantages over "Built by Lawyers" or "Built by
the guys at the local computer shop"

Perhaps the presence of smart Researchers while the computer is being born
will make the machine better at learning, improving tensorflow performance?

------
bischofs
This seems like a shameless money grab. Even the historical "enterprise" GPUs
were never this expensive. Nvidia seems like they are just taking their new
GPU design which is legitimate (Volta) and trying to cash in on the ML mania.

GPUS should be general purpose and with Nvidia throwing all their eggs in the
ML basket and charging a fortune it might risk alienating their core users
(gamers).

~~~
mathgeek
> GPUS should be general purpose

Not all GPU's need to be good at all things. There are plenty of general-
purpose chipsets out there for those that want one, and I'd say there's room
in the market for highly specialized GPU's and if those are priced
incorrectly, the market will correct it. The irony in your statement is that
this is designed to be a cheaper version of a very popular card.

------
tromp
For the Alpha Zero fans among us, how many of these does it take to match the
performance of four 2nd gen Google TPUs?

~~~
Quequau
Is it possible for a regular human to even buy four 2nd gen Google TPUs?

~~~
farresito
Speaking about buying TPUs, yesterday I vaguely remembered that the company
that sells mining ASICs like the Antminer was planning to start selling AI
chips ala Google's TPU. I checked their website and they have actually started
selling them already [https://www.sophon.ai/](https://www.sophon.ai/)

I think it's pretty exciting seeing how we are getting increasingly more
specific chips for AI tasks. I wonder what their performance is like compared
to GPUs.

~~~
DiThi
> SEC_ERROR_UNKNOWN_ISSUER

Is this because in China you're expected to ignore all certificate warnings?

> I wonder what their performance is like compared to GPUs.

Me too. I tried to find a similar table and found this:
[https://github.com/tobigithub/tensorflow-deep-
learning/wiki/...](https://github.com/tobigithub/tensorflow-deep-
learning/wiki/tf-benchmarks#alexnet)

25 ms/batch of 128 is 5120 images/second... Is that correct? Am I missing
something?

~~~
farresito
> SEC_ERROR_UNKNOWN_ISSUER

I can access it no problem with Chrome. The price seems to be 589$ per chip.

Google wrote an article some time ago about the their TPUs. There are a few
charts at the end of the page: [https://cloud.google.com/blog/big-
data/2017/05/an-in-depth-l...](https://cloud.google.com/blog/big-
data/2017/05/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu)

I would be quite interested in a comparison between Google's TPUs and this
Sophon chip, but it seems it's quite new and they have barely started selling
them, so we might have to wait.

> 25 ms/batch of 128 is 5120 images/second... Is that correct? Am I missing
> something?

I'm not particularly knowledgeable about this, so you will have to wait for
someone experienced to chime in.

------
alvern
What are the use cases for the Titan V vs. Radeon Pro SSG?

Just ML? Higher memory bandwidth?

[https://pro.radeon.com/en/product/pro-series/radeon-pro-
ssg/](https://pro.radeon.com/en/product/pro-series/radeon-pro-ssg/)

~~~
freeone3000
It's nvidia. You get CUDA and CuDNN, which means the standard suite of ML
programs. AMD has no equivalent offering.

~~~
lovelearning
ROCm and MIOpen are AMD's equivalent offerings. They've developed their own
ports for some of the popular DL frameworks.

[https://rocm.github.io/dl.html](https://rocm.github.io/dl.html)

~~~
aseipp
Very few people who do DL professionally or as casual hobbyists have the time
to sort out ports of various frameworks to work with in-progress compute
drivers/toolchain. Most engineers aren't cash strapped, they'll just buy what
they need instead of waste time, especially when the field and the tools move
so quickly. It's just not important enough when NVidia cards work well out of
the box and can be acquired at both the hobbyist low end (GTX 1060) and
professional high end (Tesla series), have proven track records, and every DL
framework works more-or-less immediately.

They need the patches to be upstream and widely maintained by the developers
(and be involved themselves) before people 'at large' will really consider
using them. The code is there, but practically doesn't exist for anyone yet,
outside of AMD developers and a minuscule amount of AMD users. This is also
true of all of Intel's ports of various DL frameworks, too. One thing NVidia
has learned is that sticking their engineers directly into the pipeline works
-- from everything from game development studios, and now to deep learning
frameworks (which have nvidia engineers).

That said, AMD cards do have some excellent advantages like unlocked FP16 that
Nvidia does not offer outside of the Tesla series, and bringing card price
down would rock. So I'm hopeful that they'll sort all this out and make
themselves competitive. I'm hoping to see MIOpen support (and if we want
ponies, an upstream ROCm driver) sometime in the next ~year for at least a few
frameworks (Caffe2 or PyTorch would be good.) But until then, CUDA, CuDNN and
Nvidia technology are really the only name in town for like +90% of people,
for better or worse.

~~~
andars
> Unlocked FP16 ... outside of the Tesla series

Titan V has HMMA, so it most likely also has full hardware FP16. It seems to
have a binned V100 chip with 3 functioning HBM2 stacks.

------
gehwartzen
It's been a while since I've built a PC; when did graphics cards physically
start looking like they were designed by a five year old?

~~~
jdietrich
I think you have some kind of selective amnesia. Graphics cards have become
far more tasteful over the past decade. Consider these gaudy monstrosities,
which are entirely representative of the gamer aesthetic circa 2007:

[http://images.dailytech.com/nimage/6446_large_8800GT-512DDR3...](http://images.dailytech.com/nimage/6446_large_8800GT-512DDR3-AMP-P.jpg)
[https://images.anandtech.com/reviews/video/ATI/2900XT/2900xt...](https://images.anandtech.com/reviews/video/ATI/2900XT/2900xt.jpg)
[https://images10.newegg.com/NeweggImage/ProductImage/14-102-...](https://images10.newegg.com/NeweggImage/ProductImage/14-102-768-02.jpg)

The gold shroud on the Titan V is a bit bling-bling, but at least the shroud
design is part of a consistent design language from NVIDIA.

~~~
madengr
My 9 YO is building a PC for Christmas, so these would be perfect. They just
need RGB.

------
TazeTSchnitzel
GV100 implies it's the full card, not cut down in any way. Huh.

------
slizard
"With TITAN V, we are putting Volta into the hands of researchers and
scientists all over the world"

Screw you NVIDIA. I've been vocal about this for a while but this is now
getting utterly ridiculous, and frankly insuting to the research community.

I don't mind that NVIDIA is ripping of the big derplearning -- and more
recently "data" \-- companies (Goog, FB, Baidu, etc.), but this greedy
bullshit attitude led to the Tesla cards all of a sudden going from expensive,
with a 2-2.5x price jump, to the ridiculously priced. In the meantime, they've
been "enabling" research by throwing peanuts at the science community with
cancer stunts [1]. However, that was just the facade, the real story was that
they'd found the cash-cow and the enthusiastic HPC/sci-comp crowd that helped
NVIDIA grow out of their sexy-witch-on-the-front-panel-of-the-GTX company box
and step into the "big compute boys' league" is no more than just another
means to convert peanuts to marketing material.

After years of complaints that there are no sensibly prices _compute_ cards
devs and independent researchers can afford, constant battles with NVIDIA to
stop crippling their management tools on GTX cards, to allow cheap cards to be
used productively for compute and development, etc. they step up their game
and release a $3k "affordable" card for "researchers and scientists". This
claim is beyond ridiculous, revolting and, as a researcher (working in HPC on
a large FOSS simulation project) this is insulting. Which researchers are they
talking about, the ones employed by the big ad, finance, companies? Definitely
not me, not us!

Time for us to start showing NVIDIA the middle finger for good! Get porting
people, support AMD, test ROCm, their openness and attitude receptive to
public feedback and criticism has impressed me recently. At the same time, we
need to push NVIDIA too: file bug reports against NVIDIA's shitty OpenCL
support, against their consumer card crippling drivers and management tools,
and resist their efforts to blackmail OEMs to stop selling servers with
GeForce.

[1] [https://www.nextplatform.com/2016/11/15/deep-learning-
superc...](https://www.nextplatform.com/2016/11/15/deep-learning-
supercomputer-approach-cancer-research/)

~~~
robotresearcher
If you can't afford the $3K card, buy the $800 card. The $800 card that
outperforms the fastest SIMD computer any amount of money could buy just a few
years ago.

NVIDIA makes fast computers and sells them for market prices. I'm not sure we
need to storm their castle with pitchforks.

~~~
slizard
Please show me where can you buy a $800 card that can be used to develop for
the _compute_ architectures; i.e. the GV100 or the GP100 for that matter.

Also, please show me how can you use those $800 cards for serious
computing/research without potentially running into silly issues and a lot of
pain (from not being able to use the "supported" standard OpenCL to running
into cooling, fan speed, clocking issues that makes reliable perf tuning a
pain, etc.).

~~~
andars
It is strange that you expect to be able to buy the server grade GPUs for
GeForce series prices and are outraged when reality doesn't meet your
expectations.

It strikes me as pretty awesome that the V100 is now available for _only_ $3k
in the Titan V, rather than ~$10k for the Tesla V100. I reckon you need to
readjust your expectations.

~~~
slizard
> It is strange that you expect to be able to buy the server grade GPUs for
> GeForce series prices Respectfully, you are clearly missing the point. You
> can buy <$500 USD Xeon Silver parts (and fairly affordable barebone
> workstation or servers to go with) and you'll get the Xeon-SP architecture
> to develop for. You can even buy the rebadged i9 parts too for ~$1000-$1200
> with both FMA unit enabled, but admittedly those are also rather expensive.

> and are outraged when reality doesn't meet your expectations.

Untrue. I want an option, any option that allows researchers to develop for
the fancy high-end chips -- especially when they claim that they want to and
are providing such an option. Computational scientist and HPC researchers who
aim to write code for, or porting community codes to machines like DOE Sierra,
Summit or future V100-based machines _need_ something that is within the reach
of a junior PI, a researcher in countries less well off than the rich western
Universities and research institutes.

In contrast, jack up the price this high means that the Facebook et. al will
still find it worthwhile, but _the grand majority_ of actual researchers,
those that they claim to be _enabling_ and _helping_ with this card will not
be able to afford it.

It all too easy to forget how the "democratization" of supercomputing was
NVIDIA's slogan very ambitious and cool [1] that has over time turned into a
marketing claim [2] that offers little in terms of bottom-up enabling the
community.

[1] [http://gpgpu.org/static/asplos2008/ASPLOS08-1-intro-
overview...](http://gpgpu.org/static/asplos2008/ASPLOS08-1-intro-overview.pdf)
[2]
[http://assets.nvidia.com/nv/tesla/pdf/NVIDIA_Accelerate%20Yo...](http://assets.nvidia.com/nv/tesla/pdf/NVIDIA_Accelerate%20Your%20Datacenter%20with%20Tesla%20P100_Whitepaper_Dec2016.pdf)

~~~
robotresearcher
$3k is within reach for most labs. It’s lot of money for an individual. But a
PhD researcher’s annual salary is probably more than 35 times that amount in a
coastal city. An ML researcher could easily be 200 times that amount.

A new assistant professor at a medium sized school in North America can afford
a few of these.

This is a cheap bit of equipment for a professional researcher.

It’s an expensive bit of equipment for an independent researcher or student,
or someone in a poorer part of the world. But why would anyone expect the best
kit for hobby prices?

~~~
slizard
I'm saddened by the arrogance you display. Does your definition of "world"
really not go beyond the "coastal cities" and "North America"? Are you
seriously assuming there are no ML researchers outside of the cozy little
world you're referring to? People in the Middle-East, Balkans, South-Asia,
etc. should not worry about "high-end kit", they should stick to "hobby"
stuff?

(In case if this all leaves you baffled, e.g. assistant professors will often
earn 2-4 TITAN V's worth of money per year in places like Eastern Europe --
and that's still on the map of the "Western world" by most definitions.)

> It’s an expensive bit of equipment for an independent researcher or student,
> or someone in a poorer part of the world.

Allow me to correct you: that is _much_ (if not most) of the world.

> But why would anyone expect the best kit for hobby prices?

Strawmen. Please read my point again (and see the concrete examples of
affordable "kit" that can be used to target the high-end HPC iron).

~~~
robotresearcher
Excellent equipment for cheap would be great. I hope you get your wish as soon
as possible.

------
llukas
Pretty soon BTC price will justify buying one ;)

~~~
CoryG89
Would be all but worthless for BTC from my understanding, need an ASIC to even
have a chance. Might be worth it for Ethereum though.

~~~
rybosome
Rumors of a near-future switch to proof of stake would discourage me from
spending large amounts of money on Ethereum mining.

~~~
jrs95
There are so many other coins that use the same GPU friendly algorithm as ETH
that your hardware wouldn’t become useless even if that did happen.

~~~
pknerd
Care to guide more?

~~~
jrs95
I don’t know specifics on what these coins are honestly but there are a few
clients like Nicehash which basically group a lot of different mining tasks by
algorithm for you. It has an idea as to which will be most profitable for your
hardware at any given time, and mines that currency for you.

~~~
FireBeyond
The same NiceHash that had every single one of their 4700 BTC stolen
yesterday...?

~~~
jrs95
Apparently. Hadn’t heard about that. R.I.P.

------
lucaspiller
And of course, somebody has already ordered one for cryptocurrency mining:

[https://twitter.com/BitsBeTrippin/status/939031211233525760](https://twitter.com/BitsBeTrippin/status/939031211233525760)

