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Tesla bulks up its GPU-powered AI super
- vardump 4y agoI wonder how Tesla's Dojo supercomputer is doing. I don't recall hearing any details about its progress lately. Maybe we'll learn something new from the Hot Chips event mentioned in TFA.
- giuliomagnifico 4y agoInterested me too. At the moment is the number 7 of the world Supercomputer by number of GPUs.
- 4eleven7 4y agoIf not, then we'll likely learn more at Tesla AI day #2, on September 30th.
- sigmoid10 4y agoTesla will be at the Machine Learning and Systems conference in 2 weeks and if not then we'll probably hear about the Dojo D1 chip at Tesla AI day which is one month later.
- dragontamer 4y agoIt never seemed to me like the custom chip from Tesla could possibly ever keep up with the economics of mass chip production from NVidia, or whoever else became #1 at neural net hardware. New chips are always fun to see and get discussed, but there are simple economic / mass design issues at play here. The Tesla custom chip just won't be made at the volume of NVidia or AMD. And Intel's attempt to join the market seems like the better approach (even if it fails). You need to get the video game crowd to fund the research and development these days. Video gamers want high speed SIMD for faster graphics. Without this source of money (and millions of chips sold per year), it looks difficult to sustainably upgrade an architecture. -------- If not the PC market, then at least try to get the console market or phone market or some other consumer electronics fad wrapped up in things.
- Geee 4y agoMaking a chip for your own use should be more optimal than a chip made for a wide range of customers. Also, you don't need to pay the hefty markup for NVidia. If you need a lot of chips, there's a point on the curve where it becomes more economical to design your own chip.
- titoCA321 4y agoYou have to invest into the people and the hardware and software to make the chip. And then you have to support that chip throughout the years. There's reason the U.S Government got out of making their own chips and laying their own fiber-cables.
- jacquesm 4y agoTesla isn't even close to those kind of volumes.
- sanxiyn 4y agoGoogle seems to be doing well with TPU without being funded by video game crowd.
- sidibe 4y agoGoogle has a ton of internal and external customers that can amortize the cost of this kind of project. They've also got lots of similar programs of developing server hardware and some organizational experience in how to hook new stuff up and keep it utilized. The whole dojo program always seemed wildly ambitious reach to me for a company like Tesla. More power to them if they pull it off but it's not a surprise to me that it's not what they're using a year after they thought it was almost ready
- vardump 4y agoIf Tesla's chip is any good, perhaps they can also sell access to Dojo supercomputer.
- 4y ago
- jeffybefffy519 4y agoI wonder how much power this thing uses and is it ran on renewables?
- axg11 4y agoWhy does that matter?
- cr3ative 4y agoPeople are allowed to have curiosity.
- dymk 4y ago“Just asking questions” != curiosity
- MonkeyMalarky 4y agoTheir question is pretty neutral and can be interpreted either way, but hey why not assume the worst?
- texasbigdata 4y ago
- walthamstow 4y agoBecause they're curious. Curiosity is what this whole website is about. What's your problem?
- axg11 4y agoFair point. I was assuming the worst.
- dmurray 4y agoThe A100 consumes up to 300W [0]. So in total 2.2 MW for the GPUs. Add the same again for CPUs, storage, networking, and cooling the whole thing and maybe it uses 5 MW. About the equivalent of 30 Tesla superchargers, of the 35,000 they operate. [0] https://www.anandtech.com/show/16792/nvidia-unveils-pcie-version-of-80gb-a100-accelerator-pushing-pcie-to-300-watts https://www.anandtech.com/show/16792/nvidia-unveils-pcie-ver...
- algo_trader 4y ago> 7,360-GPU cluster would be capable of about 100 .. petaflops How is an A100 worth "only" 17 tera-flops? There are multiple home gpus and even mobile chips rated at tera-flops. And the A100 really is a gigantic chip. I realize all about bandwidth, and full vs half precision, and inflated marketing vs actual benchmark. But can a 10-node-iphone-cluster really match an A100 for pure flops?!
- _kbh_ 4y agoThe A100 is not optimised for pure ieee-754 FP throughput it seems but for more specialised forms of FP and bandwidth. https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Cent...
- nodja 4y ago> But can a 10-node-iphone-cluster really match an A100 for pure flops?! Only if the data is local, small, easily synchronizable, doesn't require fp32 precision and a dozen other caveats. Can a 10 iphone cluster match an A100 in calculating digits of pi? Yes. Can it do backpropagation in large machine learning models? The gradients won't even fit.
- redox99 4y agoBecause its hardware is not optimized for FP32. If you're doing FP32 then a 3090 is literally faster. It's optimized for TF32, INT8, bfloat16, and other similar data types that the tensor cores like.
- make3 4y agothe number is for fp64. gpus most definitely are optimized for fp32
- tuankiet65 4y agoThat 17 tera flops is in double precision (FP64) math as benchmarked by TOP500.
- 4y ago
- retSava 4y ago> An even larger AI supercomputer – from Meta/Facebook – was detailed earlier this year. The AI Research SuperCluster (RSC) will employ 16,000 A100 GPUs, delivering more than 200 double-precision petaflops, once completed this summer. Yay, let's further our efforts into hooking people and showing them ads! /s
- bluedino 4y agoIt's interesting seeing these machines being built by Tesla, Meta, private companies. Typically these are things built by national laboratories, university research labs...
- xattt 4y agoThis is symbolic of the maturation of academic research.
- titoCA321 4y agoAcademics publish their research even when it contributes nothing to society. Private enterprises and government agencies aren't as aggressive in publishing even when the publication of their research may benefit society more than many of the stuff coming out of academia. Some do for various reasons but sometimes they don't. That's why when companies go belly up and historians have their field day, we find out stuff such as Kodak discovering the benefits of digital photography since the 1960's and certain employees within different departments were pushing for Kodak to take the lead, yet Kodak executives didn't know what to make of it and where afraid of digital photography eating into their film business. We also learn stuff such as ATT developing video phones since the 1960's but they were too early and it never go anywhere until decades later when ATT was a lesser relevant player in the telecom industry. And the Post Office foresaw the coming trend of online bill pay and email eating into their mail volume since the early 90's, yet when their they had projects that would enable senders and receivers to received mail scanned to a USPS account or pay bank statements through a USPS account for those early bill pay adopters, Postal Management did nothing with it.
- axg11 4y agoIt's a testament to the immediate applications of AI/ML. In the past, supercomputers were mainly a tool for research, with expected returns from that research in the order of years to decades. Now, as demonstrated by how large language models are generative image models are scaling, there's a reasonable expectation of short term returns from investment.
- 4y ago
- carlsborg 4y agoFB "Research SuperCluster" has 760 NVIDIA DGX A100 . Each DGX has 8 A100s. So 6,080 GPUs. Article mentions also NERSC’s Perlmutter => 6,144 Nvidia A100 GPUs, 70.87 Linpack petaflops Nvidia’s own in-house A100 system, Selene => 4,480 A100 GPUs, 63.46 Linpack petaflops
- onion2k 4y agoCan it run Doom?
- mvdwoord 4y agoWill it blend?
- randcraw 4y agoWhat does Tesla use this for, other than full self driving? Other FAANGs would use such a beast to serve myriad tasks, from vision to text to speech. But Tesla likely licenses all their non-FSD AI tech from 3rd parties since it doesn't motivate the sales of their products the way FSD does. Meanwhile, FSD is installed on only 7% of Tesla's cars, down from over 50% in 2019. https://www.torquenews.com/1083/teslas-low-fsd-take-rate-offers-growth-opportunity https://www.torquenews.com/1083/teslas-low-fsd-take-rate-off... Thus a 70 petaflop ultracomputer is quite a big investment to support an increasingly unpopular car option that's inessential and mostly promotional, ergo, a geek tickler.
- panick21_ 4y ago> licenses all their non-FSD AI tech from 3rd parties Tesla is well known to be one of the most vertically integrated companies in the industry. The are well known to use AI for their factories as well. The run lots of simulations for crash testing as well. What products are they are buying? They do probably buy somethings but unless you have evidence that they buy most things, I not gone believe you without some kind of source. > Meanwhile, FSD is installed on only 7% of Tesla's cars You realize it can be bought in later as well right? If quality improves lots of people have to option to buy it. > that's inessential and mostly promotional, ergo, a geek tickler. That's your opinion, an opinion that Tesla clearly doesn't share.
- syntaxing 4y agoSuper curious to read more about how Tesla uses AI in their factory and design! From what I know, solvers used in “traditional” programs are heavily optimized for CUDA and Intel chips. Like decades and hundreds of PhDs has dedicated their lives just to make that happen. So curious how Tesla replaced or improved on that.
- keneda7 4y agoNot the person you replied to but I did find a video that talks about AI at Tesla Gigafactory here: https://www.youtube.com/watch?v=fmFv34SC9OQ https://www.youtube.com/watch?v=fmFv34SC9OQ Original site I found the video at: https://www.supplychaintoday.com/tesla-artificial-intelligence-manufacturing-revolution/#:~:text=The%20Tesla%20artificial%20intelligence%20manufacturing,comes%20to%20their%20supply%20chain https://www.supplychaintoday.com/tesla-artificial-intelligen.... More info: https://www.google.com/search?q=tesla+using+ai+in+factorys&rlz=1C1GCEA_enUS959US959&oq=tesla+using+ai+in+factorys&aqs=chrome..69i57j33i10i160l3.5293j0j7&sourceid=chrome&ie=UTF-8 https://www.google.com/search?q=tesla+using+ai+in+factorys&r...
- bigtex 4y agoToo bad it can't stop the cars from running over small children. All that gpu power wasted.
- panick21_ 4y agoThat video was clearly faked, I mean it wasn't even faked very well. You can even see how FSD wasn't enabled at one point you can see the car flashing warning signs (but the audio of course was removed so people wouldn't notice). The driver manually over-wrote the system and that behavior is clearly explained in the user-manual and its how the car is supposed to work. Do you always take internet videos made by literal competitors over the state licensed regulators?
- Sohcahtoa82 4y agoGot a link to said video? I'd like to judge for myself.
- xedeon 4y agohttps://youtu.be/n6ZWr7uiBPE?t=474 https://youtu.be/n6ZWr7uiBPE?t=474
- Sohcahtoa82 4y agoWow that's pretty damning.
- xedeon 4y agoYeah, there were so many red flags that don't seem obvious to the untrained eye: 1. No clear indication that it was even on FSD. Only TACC was probably engaged. 2. Video was posted in 480p so it's hard to see what's on the screen. 2. YouTube Comments were turned off. 3. Bottom half of the screen was cut off on the initial video (most glaring omission IMO). Which would show if the warning that the accelerator pedal was being held down and that the vehicle would not stop. It looks like this: https://i.imgur.com/G3l9Rp1.png https://i.imgur.com/G3l9Rp1.png 4. Additional footage that was lasted released (also in 480p it's 2022, why?) CONFIRMED this: https://i.imgur.com/1PMbG9f.png https://i.imgur.com/1PMbG9f.png These are just a few examples. But it's hard to believe that the video was not made in good faith.