NVIDIA shows signs ... [2008 - 2017]

[pharma]

Planning to commercialize cars equipped with NVIDIA's AI within one year - NVIDIA and Mercedes

The joint development of the two companies has been underway three years ago, according to Jen-Hsun Huang, founder and CEO of NVIDIA, said that it will be able to reach commercialization in the next year, and Mercedes · Benz Sajjad Khan, who is Vice President of Digital Vehicle and Mobility (Vice President of Digital Vehicles & Mobility) also explains that within 12 months it will release jointly developed products.

In this joint development, collaboration centering on deep learning and artificial intelligence is being promoted by joint team of both companies, according to Jen-Hsun Huang, AI will change future cars I will comment that it will be.

https://translate.google.com/transl.../articles/2017/01/19/nvidia_volta/&edit-text=

 

[pharma]

Nvidia enjoys record revenue, up 55 percent from a year before
In a summary set of bullet points Nvidia's better than expected results can be summed up as follows:

• Record quarterly revenue of $2.17 billion, up 55 percent from a year ago
• Record full-year revenue of $6.91 billion, up 38 percent from a year ago
• Record quarterly GAAP gross margin at 60.0 percent, non-GAAP gross margin at 60.2 percent
• GPU computing platform continues to power gains across full product line

Nvidia's GPUs for PC gaming, still contributes more than three-quarters of its total revenue. This segment's business performance has been remarkable, up 57 per cent during the year. Furthermore, over the year, revenue from professional visualization was up 11 per cent, data centre soared 138 per cent, and automotive ascended 52 per cent. Analyst Patrick Moorhead Tweeted about how impressed he was with Nvidia's overall performance.
....
Nvidia's stock value has risen from $25 a year ago to $116 today.

http://hexus.net/business/news/components/102367-nvidia-enjoys-record-revenue-55pc-year/

 

[pharma]

AI helps Nvidia reach record revenues

Nvidia highlighted how AI is transforming industries worldwide starting with hyperscale companies like Microsoft, Facebook and Google, which use services that utilizes image recognition and voice processing. The next area of growth will occur as enterprises in such fields as health care, retail, transportation and finance embrace deep learning on GPUs.

Deep learning has been a strong area of interest for Nvidia over the past few years, with Nvidia CEO Jen-Hsun Huang explaining that it is a breakthrough technique in the category of machine learning, and machine learning is an essential tool to enable AI. Deep Learning is the technique where software can write software by itself, by learning from a large quantity of data. Prior to deep learning, other techniques like expert systems, rule-based systems and hand-engineered features -- where engineers would have to write algorithms to figure out how to detect specific things, like a cat or a face -- were labor intensive, slow to progress and provided limited results, Huang explained.

The reason why deep learning took a long time to come along is because its singular handicap is that it requires an enormous amount of data to train the network, and it requires an enormous amount of computation. And now deep learning has proven to be quite robust. It is incredibly useful, and this tool has at the moment found no boundaries of problems that it's figured out how to solve, Huang said.

Huang does believe that traditional methods of machine learning are still going to be useful if the absolute precision of the prediction or classification is not necessarily super important. For example, if you wanted to understand the sentiment of consumers on a particular new product that you sent. So long as marketers understand the basic trend, they would consider that information useful.

However, for cases where high the precision needs to be highly accurate, such as in cancer detection or in other medical areas, or for financial services or in high-performance computing, small differences in accuracy could make a very large difference in the final results, Huang explained. In these cases, deep learning has been found to be a great utility. Huang then cited Nvidia's leadership in self-driving cars as being an example where it is required, and pays off, to be exactly right.

Huang added that Nvidia is getting a lot of credit for helping deep learning break through due to the company's programmable GPUs and GPU computing platform.

http://www.digitimes.com/news/a20170210VL200.html

 

[pharma]

Some Perspective: Zarathustra's Nvidia Price History


"As we can see from this chart, current pricing for the 1080 Ti is pretty much inline with where NVIDIA has typically been. When adjusted for inflation the 1080 Ti almost exactly matches the price of the GeForce 2 Ultra from back in 2000. We have some notable fluctuation over the years, which mostly seems to coincide with when NVIDIA had true competition in the market place. When NVIDIA were on top, and the competition had nothing, the prices went up, as we can see with the 8800 Ultra. Other times, during periods of higher competition in the market, pricing was lower. You could argue that the 1080 Ti is actually under-priced for the market climate. Argue whichever way you want about the appropriateness of NVIDIA's pricing, but this information does show a trend much in line with the relative market position of the brand."

http://www.hardocp.com/news/2017/03/10/some_perspective_zarathustras_nvidia_price_history

 

[Silent_Buddha]

Except for one thing. The Ultra is more analogous to the Titan line of GPUs, pushing both performance and price. The Ti branded cards were more analogous to the Ti branded cards.

The Geforce 2 Ultra (~700-ish inflation adjusted USD), 5950 Ultra (~656 USD), 6800 Ultra Extreme (~693 USD) and 8800 Ultra (~992 USD) should be compared to the Titan X (1200 USD).

They would have made a better case to have compared it to the GTX 280 (~720 USD), a consumer enthusiast card versus the more limited extreme high end cards that the Ultras represented at the time.

Regards,
SB

 

[entity279]

Depends wrt to Titan, one of its earliest iterations had 1/3 DP peformance. That thing specifically is not directly comparable with the old Ultra suffix.

 

[lanek]

Depends wrt to Titan, one of its earliest iterations had 1/3 DP peformance. That thing specifically is not directly comparable with the old Ultra suffix.

Well, yes and no, not so long time ago, there was not much difference between High end gpu's and professional grade one ( ofc ECC memory etc. ) But the high end GPU was in general the exact same ( same DP performance etc ) that the professional one.

 

[manux]

Nvidia did very good on the beginning of the year

The company's net income more than doubled to $507 million, or 79 cents per share, in the quarter ended April 30.

http://www.reuters.com/article/us-nvidia-results-idUSKBN1852GB

Link to tomorrows gtc keynote from nvidia(9am pst). It will be curious to see what if anything gets announced

http://www.ustream.tv/gpu-technolog...id=861680041138167808&adbpl=tw&adbpr=61559439

 

[pharma]

Nvidia says Toyota will use its AI technology for self-driving cars

Nvidia Corp announced a partnership with Toyota Motor Corp on Wednesday, saying the Japanese car maker would use Nvidia's artificial intelligence technology to develop self-driving vehicle systems planned for the next few years.

http://www.reuters.com/article/us-n...ters/technologyNews+(Reuters+Technology+News)

 

[pharma]

Toyota Selects Nvidia, Intel Feels Heat

According to Egil Juliussen, director research, Infotainment & ADAS at IHS Automotive, Toyota has become the fourth major car OEM publicly committed to Nvidia’s Drive PX for their highly automated vehicle. The other three OEMs are Audi, Daimler, and VW Group.

In addition to those OEMs — which include the world’s two biggest carmakers Toyota and VW, Juliussen added that Nvidia also previously picked up smaller OEMs including Volvo, Tesla and Nio (formerly known as NextEV). Since tier ones such as Bosch and ZF have also embraced Nvidia’s hardware platform, Juliussen believes that this “will probably help Nvidia getting other OEMs on board.”
....
Asked why Nvidia appears to be gaining partners, many industry analysts pointed out that OEMs and developers are a little turned off by Mobileye/Intel’s closed approach to the automated vehicle platform.
The Linley Group’s Demler said, “We’re seeing tier ones and carmakers signing up to use Drive PX because it gives them a better development platform than Mobileye’s closed system.”

Noting that Intel’s Mobileye acquisition will take months to close, Demler said, “There currently is no competitor that can offer the same platform for development, training, and inference.” He added, “It’s doubtful Mobileye-Intel will open up their platform to match CUDA-DNN, Drive PX, etc.”

Asked about the Intel/Mobileye/BMW platform, Magney said that its approach is similar to Nvidia’s, but the architectures are different.

He said, “Led by the processor companies, we are seeing a race to provide as much of the AV [automated vehicle] stack as possible. Rather than a node here or there, the big chip companies are all trying to assemble an eco-system of hardware, software and development tools. They are all offering up "platforms" with the hardware and software components, plus the development tools and simulations to build up the solutions.”

Nvidia’s lead in picking up more design wins can be also explained by the fact that it is seen as “the market leader in democratizing AI,” Magney observed.
http://www.eetimes.com/document.asp?_mc=RSS_EET_EDT&doc_id=1331727&page_number=1

 

[Geeforcer]

Signs.

 

[pharma]

Nvidia Rolls Volta GPU For 'AI Revolution' (11 pages)

Blue River Technology drove a John Deere tractor (above) onto the show floor to carry its trailer currently used to automate planting for 10% of the U.S. lettuce crop. Its next goal is to find use in weeding cotton fields.

The trailer uses one camera to detect precise locations to plant or spray (below). A second camera measures effectiveness and recalibrates the system as it moves. Nvidia’s Jetson modules power the computer vision system for the venture-backed startup.

http://www.eetimes.com/document.asp?doc_id=1331729&_mc=RSS_EET_EDT

 

[pharma]

Excellent article.

Nvidia does not often break out financial or shipment data for the Tesla compute and related GRID virtual visualization engines, but we catch snippets here and there.

Back in 2008, when the Tesla line was launched, the company shipped 100 million GPUs that were capable of running the Compute Unified Driver Architecture that had launched in 2006; it had over 150,000 downloads of CUDA by the spring of 2008 and it sold 6,000 units of its “Fermi” family of Tesla GPU accelerators. Fast forward to the spring of 2015 and a total of 576 million CUDA-capable GPUs had been sold and of these, some 450,000 were Tesla branded accelerators that were being used by HPC centers, hyperscalers, clouds, and selected enterprises; CUDA had over 3 million downloads by the GPU Technology conference in the spring of 2015, and the rate was running at something like 1 million in the prior 18 months. At this year’s event, Huang did not brag about cumulative shipments of Tesla accelerators, but did say that people downloaded over 1 million instances of CUDA in 2016. Over 20,000 people attended one of several global GTC events in 2016, and Huang said Nvidia estimated that there were over 511,000 GPU developers in the world today, more than a factor of 11X compared to back in 2012.
....
Back in May 2015, Nvidia’s top brass talked about the total addressable markets that it was chasing beyond PC graphics, still its biggest product line and still growing like crazy. This bears repeating as we plot out possible growth curves for Nvidia. The company reckoned back then, two years ago, that there was a $5 billion opportunity for GPU sales within the overall $100 billion gaming industry; GPUs comprise about a third of a console’s cost, but the industry is mostly software, not hardware. There is another $5 billion in processing and GPUs for the auto industry, a subset of the $35 billion overall computing bill for the car makers. Enterprise visualization, which includes Quadro graphics cards and GRID remote visualization motors, had about $1.5 billion in potential sales to professional designers and another $5 billion for centralized datacenter services. Then the core compute business represented by Tesla (and of course other GPU cards) and represented by HPC centers and hyperscalers but quickly moving into the enterprise was a $5 billion opportunity. Add it all up, and Nvidia said it was chasing markets worth $21.5 billion. In the trailing twelve months, Nvidia booked $7.54 billion in sales, so it already has captured about a third of its addressable markets, which have probably expanded a bit in two years. Particularly as machine learning goes mainstream and as the new Volta GPUs do an excellent job on machine learning inference, not just training. So let’s be generous and say that the market is actually approaching $30 billion and Nvidia has captured about a quarter of its addressable market here in 2017.

The point is, there is room to grow, and Nvidia can double its share and double its revenues and still not have the majority of the markets even if it is dominant. This seems likely, and with all of the competition, it seems unlikely that Nvidia can capture more than 50 percent share. That would still make Nvidia a $15 billion business. And based on our rudimentary models, that is precisely what we think could happen as Nvidia closes out its fiscal 2019 year in January 2020.

https://www.nextplatform.com/2017/05/16/embiggening-bite-gpus-take-datacenter-compute/

 

[manux]

I have a feeling this is indeed true.

Rasgon notes a comment from CEO Jen-Hsun Huang at the company’s analyst day a week ago, "one of the boldest, and honest, statements we've ever heard senior management of a company make in public,” he writes, "This is all going to work out great, or terribly, for us because we're all-in."

The data center chips or machine learning "is likely somewhere between 'big,’ and ‘huge’,” writes Rasgon, "driven by the mainstreaming of AI and the rise of accelerated computing."

The Automotive use "has not even gotten ‘good’ yet (still dominated by infotainment),” he writes.

http://www.barrons.com/articles/nvidia-this-could-work-out-great-says-bernstein-1495173785

 

[pharma]

JPR: Moderate sales in the first quarter, the GPU industry starts to gear up for Q3

Quick highlights
AMD's overall unit shipments decreased -24.81% quarter-to-quarter, Intel's total shipments decreased -13.91% from last quarter, and Nvidia's decreased -25.64%.

• The attach rate of GPUs (includes integrated and discrete GPUs) to PCs for the quarter was 136% which was down -4.75% from last quarter.
• Discrete GPUs were in 31.36% of PCs, which is down -4.57%.
• The overall PC market decreased -14.65% quarter-to-quarter, and decreased -1.74% year-to-year.
• Desktop graphics add-in boards (AIBs) that use discrete GPUs decreased -29.83% from last quarter.

Q1'17 saw a no change in tablet shipments from last quarter.

The first quarter is, on average, flat to down from the previous quarter, reflecting seasonality which has finally seemed to reestablish itself in the market.

The enthusiasm for tablets has subsided, and the PC market seems to have stabilized as users realize a tablet is useful for a lot of things, but can not replace a PC for performance, screen size, or upgradability.
...


GPUs are traditionally a leading indicator of the market, since a GPU goes into every system before it is shipped, and most of the PC vendors are guiding cautiously for Q4'14. The Gaming PC segment, where higher-end GPUs are used, was a bright spot in the market in the quarter.

The global GPU market demand in Q1'17 decreased from the previous quarter, and decreased from last year, to 82.67 million units. However, as Figure 5 shows, While PC shipments have returned to predictable patterns, graphics shipments have been steadily increasing over time.

http://jonpeddie.com/press-releases...up-for&usg=ALkJrhhusQhJQqnz0IgcxEjkfPFQ_7GASg

 

[Silent_Buddha]

JPR: Moderate sales in the first quarter, the GPU industry starts to gear up for Q3




http://jonpeddie.com/press-releases...up-for&usg=ALkJrhhusQhJQqnz0IgcxEjkfPFQ_7GASg

Some interesting data points from that. Discrete continues to have YoY declines, but integrated has had a YoY increase.

I wish they provided YoY numbers for the following, but all they gave us was QtQ numbers.

Desktop discrete for AMD decreased by 34.6% QtQ while notebook discrete decreased by 16.0% QtQ.

Desktop discrete for NVidia decreased by 27.0% QtQ while notebook discrete decreased by 23.0% QtQ

Overall shipments decreased by 24.81% QtQ for AMD while it decreased by 25.64% QtQ for NVidia.

This emphasizes how AMD isn't terribly competitive in the desktop discrete space at the moment as they have no counters to NVidia cards at the higher gaming performance tiers. Conversly, they are much more competitive in the notebook discrete space. The latter is interesting as NVidia should have the more power efficient architecture. However, as the notebook market is price sensitive AMD is likely offering a better price/performance proposition than NVidia at the moment which is making them more attractive to OEMs and price conscious notebook buyers despite their deficit in perf/watt. NOTE - this isn't necessarily a reflection of who has the greater volume of shipments but the volatility of their shipments from quarter to quarter.

Regards,
SB

 

[CSI PC]

Well this is something to take note of regarding SoftBank.
SoftBank Takes $4 Billion Stake in U.S. Chipmaker Nvidia, Sources Say: https://www.bloomberg.com/news/arti...-take-4-billion-stake-in-u-s-chipmaker-nvidia

SoftBank own ARM these days, this is possibly more of an indirect investment in what SoftBank sees as a strong tech company, but also worth considering Nvidia does use ARM in their more forward looking tech.

Cheers

 

[manux]

Looks like even google is still in volta bandwagon

Companies that have stated their enthusiasm and planned support for Volta-based services include Amazon Web Services, Baidu, Google Cloud Platform, Microsoft Azure and Tencent.

The Green500 list, released today at the International Supercomputing Show in Frankfurt, is topped by the new TSUBAME 3.0 system, at the Tokyo Institute of Technology, powered by NVIDIA Tesla P100 GPUs. It hit a record 14.1 gigaflops per watt -- 50 percent higher efficiency than the previous top system -- NVIDIA's own SATURNV, which ranks No. 10 on the latest list.

Spots two through six on the new list are clusters housed at Yahoo Japan, Japan's National Institute of Advanced Industrial Science and Technology, Japan's Center for Advanced Intelligence Project (RIKEN), the University of Cambridge and the Swiss National Computing Center (CSCS), home to the newly crowned fastest supercomputer in Europe, Piz Daint. Other key systems in the top 13 measured systems powered by NVIDIA include E4 Computer Engineering, University of Oxford, and the University of Tokyo.

http://nvidianews.nvidia.com/news/n...s-top-13-most-energy-efficient-supercomputers

Dell EMC will work with Nvidia to support the new Volta GPU architecture and intends to launch Volta-based accelerators by the end of the year.

http://www.zdnet.com/article/dell-emc-and-nvidia-sign-gpu-deal-aimed-at-hpc-data-analytics-and-ai/

 

[entity279]

AWS offered Kepler based services for a good while now. I don't know about the others, but for Amazon is just a simple , welcome and expected upgrade

 

[manux]

Zenuity, as a new entity, will provide the resulting self-driving software from the partnership to Volvo directly, while Autoliv will also sell the same software to third-party OEMs using its existing supply channels and relationships. It’s great news for Nvidia, too, since that means their PX platform will be a key ingredient for OEMs looking to implement the system in their own vehicles.

https://www.google.com/amp/s/techcr...driving-cars-with-nvidia-ai-tech-by-2021/amp/