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It's not that Nvidia 'keeps winning', Nvidia failed mobile computing, failed embedded computing, had no market share in the server market or data centers, was l

• bigdict 3y ago
  > it probably did not expect and plan for this You'll find out when you read TFA!
• brigadier132 3y ago
  > then its parallel GPU got "lucky", first the bitcoin mining, then the AI. it probably did not expect and plan for this, to some extent, it got super lucky. I feel like saying they got "lucky" after trying and failing in multiple other endeavors requires a special definition of luck. If someone rolls a 6 sided die six times and they roll a six once did they get lucky?
  • hsbauauvhabzb 3y ago
    Being good also has the facade of luck, every venture is a gamble none of which are guaranteed, but putting yourself in the most optimal positions (including diversifying) will result in some successes and some failures. You don’t end up with a $1t valuation based purely on luck.
  • deleted 3y ago
    [deleted]
  • nradov 3y ago
    For corporations, just staying alive that long without a huge hit is very lucky. Look how many other chip companies failed or were acquired cheaply during that period.
  • Rapzid 3y ago
    Well, you could roll the dice 1000 times and not get a 6.
    • semireg 3y ago
      The problem with randomness is “Randomness does not look random” - from the book Fooled by Randomness
    • oh_sigh 3y ago
      I bet you couldn't.
      • Rapzid 3y ago
        I'm too lucky.
• nightski 3y ago
  Your are joking, right? A niche market like gaming? lol
  • chrischen 3y ago
    PC gamers with GPUs is probably much smaller than the console gaming market. And Nvidia did not entirely own that market either.
    • jayd16 3y ago
      Nvidia also powers the Nintendo Switch.
    • eek2121 3y ago
      You would cream your pants if you happened to have even 10% of the PC gaming GPU market. Like literally. Stop downplaying markets because of X. The market-share of the PC gaming market is higher than most modern countries of the world.
    • TulliusCicero 3y ago
      Not so sure about that, I'd expect prices/margins are much better on PC GPU's compared to the deals struck with console makers.
    • elefanten 3y ago
      Historically, it was typically quite a bit smaller than consoles. Recently, they’re pretty similar in size but both significantly smaller than mobile.
  • scns 3y ago
    With a higher revenue than the movie industry.
  • Gordonjcp 3y ago
    Video is a niche market, and NVidia has had that sewn up for 20 years or so now.
• tverbeure 3y ago
  Nvidia got lucky because every quarterly all-hands meeting Jensen repeated that he kept on investing in CUDA and adding more silicon to the GPUs than strictly needed because, one day, an application would come along that would make it all worth it. TFA says that Nvidia started aggressively seeding CUDA and GPUs for research in the early 2010s. It was much earlier than that: it started pretty much immediately after CUDA was introduced late 2006. And every new generation there were hardware features added to make GPU programming and porting of applications less painful. The first Nvision conference, precursor of GTC, was in 2008. That’s how you make your own luck. I’ll never forget when, sometime around 2012?, he answered the question: “aren’t you afraid of Intel?” His answer: “Not at all. Intel should be afraid of us. We will be bigger than them.” There was not a trace of doubt.
  • 88j88 3y ago
    Some years before CUDA there was a lot of hype when the first GPGPU papers published in 2003 which showed significantly increasing performance using parallel computation from consumer graphics cards. At the time, it looked like competing on general purpose computation was a solid strategy: multi-core CPU from intel was still years away, showing up in 2005; starting from 2000 the rate of increase of clock speeds started slumping. We saw Intel started releasing more variants of processors, but the clock speeds weren't advancing exponentially anymore. The new battle for core supremacy was on the horizon.
    • pjmlp 3y ago
      I have papers collecting digital dust already doing compute with GPGPU assembly language. We already knew some of the possibilities when looking at Renderman, or early GPGPU attempts like the TMS34010.
  • kibibyte 3y ago
    Oh interesting. I remember that Folding@Home way back then (ca 2009) was already testing protein folding on GPUs and it took advantage of CUDA. I never really thought much of it other than how cool it was that my mid-tier Nvidia graphics card could be used for something else other than games, but this explains how this ended up happening. (Bit of a tangent, but that project was very influential in getting me interested in computer science because, wow, how cool is it that we can use GPUs to do insane parallel computing. So I guess, very very indirectly, Nvidia had a part in me being a software engineer today.)
• dilyevsky 3y ago
  No market share in server market if you dont count all those server GPUs and NICs and core/spine/tor switches and so on…
  • foobiekr 3y ago
    They barely exist in the network switch market. The new NVLink switches may change that for tier one inter-chassis transport to replace Infiniband RDMA, though. Reality is Mellanox has never been a substantial networking player. Yes, in HPC, but that's a tiny market even today. It doesn't help that Mellanox acquired Cumulus just prior to Nvidia acquiring Mellanox, and Cumulus was basically DOA. Now they are split SONiC/Cumulus with a lot of internal infighting trying to keep Cumulus relevant despite industry trends.
    • dilyevsky 3y ago
      Heh yeah sonic seems to be eating cumulus lunch downmarket but upmarket maybe not?
      • foobiekr 3y ago
        Cumulus is dead. Mellanox only bought it because there wasn't much else. I another 2-3 years it will be as relevant as Vyatta.
  • scns 3y ago
    And 5 super computers in the top ten, starting at fourth place behind two Cray/AMD and one Fujitsu: https://top500.org/lists/top500/2023/06/ https://top500.org/lists/top500/2023/06/ [edit] correct the cut off link
• causality0 3y ago
  Don't forget the drivers. As someone who just switched from Nvidia to AMD, it is downright painful how bad AMD's implementations of Vulkan and OpenGL are. I might be getting more bang for my buck but damn do I miss not having unfixable glitches.
  • synergy20 3y ago
    AMD is good at being the underdog, hopefully it will focus more on its software, the ROCm thing, which really needs some love, a lot of love indeed. The software ecosystem for AMD's RDNA(gpu) and CDNA(MI2xx MI300) is at its best a mess. AMD shall own OpenCL and boost it heavily and make it a central piece for its ROCm framework as the preferred backend, in my opinion.
    • pjmlp 3y ago
      No one cares about OpenCL and its C only programming model.
      • thou4o2i34u234 3y ago
        OpenCL is deprecated. Apple has ditched it for metal. AMD has ditched it for ROCm which is just a AMD compiler for CUDA PTX.
        • zorgmonkey 3y ago
          ROCm does not compile PTX IR to AMD assembly, if it did you'd be able to run nearly any compiled CUDA program on and AMD GPU without the source. ROCm is a source level compiler for CUDA (technically HIP is the actual compiler, but whatever), it allows you to a substantial fraction of the CUDA APIs. This notably also means that any Nvidia open source library that uses inline PTX assembly won't work, but fortunately AMD does have alternatives to many of the Nvidia libraries.
        • synergy20 3y ago
          it's not, Nvidia just added support to OpenCL 3.0, AMD also continues to support it. Apple always does things its own way.
• dotnet00 3y ago
  It's a bit unfair to ascribe it to sheer luck. They were focused on the compute related possibilities all the way back with the GeForce 3 in 2001. Presentations from its launch were already talking about the potential of a "parallel compute monster" [1]. They saw the potential of GPU compute very early on, invested in it long term and as a result eventually ended up dominating the market. The others didn't seriously commit and so they fell behind. AMD still can't seem to commit, while Intel seems to be working hard on catching up but isn't quite there yet. [1] https://developer.download.nvidia.com/assets/gamedev/docs/GF3ArchitectureOverview.pdf https://developer.download.nvidia.com/assets/gamedev/docs/GF...
  • kijiki 3y ago
    The actual quote from your link is: "Expect a massively programmable, massively parallel and pipelined graphics monster", not a "compute monster". While I agree that Nvidia positioned themselves well, they were not looking as far forward as you suggest. As I recall it, they seemed surprised by BrookGPU, though they moved quickly to embrace the model. And there were earlier proto examples of GPU compute that that were completely overlooked: https://web.archive.org/web/20010607021839/http://freespace.virgin.net:80/hugo.elias/radiosity/radiosity.htm https://web.archive.org/web/20010607021839/http://freespace.... (scroll down to "Optimising with 3D Rendering Hardware")
• nirui 3y ago
  I thought that I should mention a book because it got talked a lot under Nvidia's "lucky success". The book is titled: Why Greatness Cannot Be Planned: The Myth of the Objective (https://link.springer.com/book/10.1007/978-3-319-15524-1#toc https://link.springer.com/book/10.1007/978-3-319-15524-1#toc). I don't really think Nvidia's luck is really just a luck. After all, they has the vision for CUDA, bet money on it, and succeeded. They CAN stop the effort at any point during the adventure, but they choose to continue. That's not luck, that's a vision. BTW, the same book got picked up by some Chinese talk heads as an perfect demonstration showing the good side of capitalism, which I totally agreed. After all, if Nvidia was a Chinese company, they'll probably instead put their efforts into developing some massive-spying citizen-incriminating policewrongs-neverseening camera with funding provided by the government, thus almost guaranteed "success". I'm really glad that Nvidia is operating in a country where "making hardware so everyday people can use their computers to play games" is not something to be mocked at by it's (abusive)parents-acting government. Now, look who's paying smugglers for those A100 chips at double the price while crying? A well deserved fate on my book. So again, "lucky success" maybe not just luck.
  • pjmlp 3y ago
    Yeah, the usual overnight success, 7 years in the making, kind of luck.
  • fragmede 3y ago
    There's a lot of hard work that goes into luck, but one aspect of the Nvidia story is of AMD's mismanagement. There's an alternate reality where OpenCL became the default instead of CUDA but that's not our reality.
  • sledgehammers 3y ago
    I started reading that book (seems great!) and stumbled upon this: "And it’s hardly clear that computer scientists will succeed in creating a convincingly-human artificial intelligence any time soon." Made me smile. Took but 8 years, the book was published in 2015. :) (BTW. I'm super concerned about all the incredible amount of power nvidia has now and in the future. They get to decide the fate of the human race, I feel. And their incentive is just to make as much money as possible, while all negative externalities including the likes of extinction is left to the society to deal with. Sigh.)
• dylan604 3y ago
  >then its parallel GPU got "lucky", first the bitcoin mining...credit must be given to its CUDA ecosystem this seems like a bit of contradiction to me. they either got luck as you claim, or CUDA was good to keep things going. from my experience, CUDA was the only way to go for GPU accelerated processing. specifically, my experience was with Resolve color correction and using CUDA was the only way to go. memory is fuzzy, but the timeline on crypto was sametime-ish. because of the audience, this forum is naturally going to trend crypto vs niche video post workflows, but CUDA was definitely kicking Radeon/AMD ass in other aspects besides useless crypto.
  • wmf 3y ago
    I remember when miners were complaining how unfair it is that Radeons mined faster than GeForce but that was basically prehistory at this point.
  • Gordonjcp 3y ago
    Same. I wanted to buy a really chunky graphics card for BMD Fusion, but the price got pushed up by the cryptobro wankers mining their Dunning-Krugerrand, and now the price is getting pushed up by all the chatgpt wankers trying to run hardware-accelerated Eliza bots.
• rob74 3y ago
  I won't comment on the actual facts, I'll just say that I couldn't bear reading more than the first few paragraphs of the article because both guys sounded like such rabid fanbois...
• foobiekr 3y ago
  By reputation, Jensen doesn't really have much in the way of SW understanding, and in that sense he is like a lot of former chip guys. They got _insanely_ lucky that CUDA took off and there's an amazing irony in SW being their primary lock on their current market.
  • empiricus 3y ago
    To win, they just needed to show up. Which is more than can be said about the competition. AMD alternatives to CUDA are/were fumbling in the dark for many years, and more open alternatives like OpenCL are too limited (by design?). To me the situation looks quite clear: a GPU has vastly more compute than a CPU. As time goes, we will need and use more and more compute. You just need a way (general purpose language or API) to use that GPU. For some reason, other companies in this space did not see this.
    • pjmlp 3y ago
      They keep failing to see this, while CUDA is a polyglot programming model, a couple of years ago at an OpenCL conference (IWOCL), someone asked the panel when Fortran support was going to happen. Everyone on the panel reacted surprised that it would be something that anyone would want to do, and most of the answers were the kind of talk to us later. Meanwhile PGI was shipping Fortran for CUDA, this was before them being acquired by NVidia.
    • foobiekr 3y ago
      I agree with this. All they had to do was the bare minimum and actually keep it alive for a few years. This pattern is pretty common in industry. Almost all the huge companies that are winners in technology are those that got on the market and kept the thing alive - that's not sufficient, but it is necessary.
    • formerly_proven 3y ago
      CUDA and close to metal happened at the same time. One is a framework, language extensions etc., the other is “heyy here’s an assembler for this year’s GPUs” Also in ~2008 nVidia already made dedicated hardware for GPU compute like this thing: https://www.nvidia.com/docs/io/43395/d870-systemspec-sp-03718-001_v01.pdf https://www.nvidia.com/docs/io/43395/d870-systemspec-sp-0371...
• raincole 3y ago
  By this logic all companies' success stories are luck. I mean, I've met people who think this way, but it's HN...
• 0xDEF 3y ago
  Crypto miners were never "Nvidia-only". Probably because hashing algorithms were trivial to implement on OpenCL and later mROC. Nvidia's moat is AI.
  • pjmlp 3y ago
    Nvidia's moat is a polyglot GPGPU programming environemnt, great tooling and libraries.
  • nullc 3y ago
    Nvidia gpus were massively inferior for Bitcoin mining, in fact, because ATI/AMD had some integer operations that allowed SHA256 to be several times more efficient. Ironically, that's what ultimately made Nvidia the winner for gpu mining: ATI gpus had been massively deployed for Bitcoin mining prior to the dominance of mining asics. When people created new altcoins they specifically designed their work functions so that they the inventors could have an advantage vs the general public, so they designed them for nvidia gpus rather than what was already deployed. This let them buy up gpus before shortages came into effect and delayed competition from the installed base. Sure, you can trivially port whatever to whatever, but outside of startup effect mining is naturally perfectly competitive. Being 20% less efficient vs costs means bankruptcy. > Nvidia's moat is AI. Nvidia had a gpu computing moat before the current AI fad, due to maturity of the CUDA ecosystem. At least AI codes are generally pretty easy to port to other architectures, similar to mining in that sense-- but the AI designers don't have a profit motive to make sure they choose algorithms that are more efficient on their hardware than yours, and your AI hardware doesn't become useless if does happen to be a few percent less efficient.
    • paulmd 3y ago
      It totally depends on the era and the algorithm. The first big mining wave was 2014-2015. This was done on 5850s, and GCN 1 and 2 GPUs. This was Bitcoin, so it was computation-focused, and I think in this era it was definitively AMD dominated due to VLIW allowing very dense execution resources plus early GCN having a large amount of raw integer processing power. The next was 2017-2018. By this era bitcoin itself had moved off to FPGAs and ASICs, so this was around Ethereum which primarily worked based on proof of memory bandwidth. AMD GPUs were falling behind Maxwell/Pascal in terms of their memory compression (although they did use it) so they were equipped with more memory bandwidth to compensate. So for a given AMD card (Polaris, Vega, etc) you got more memory bandwidth per $, but in terms of the actual compute efficiency, NVIDIA had already pushed ahead even in Ethereum. NVIDIA was usually superior per-watt with cards like 1060, 1070, 1070 Ti, and 1080 Ti, and AMD just let you burn more watts. However when it came to altcoins, where it was not just raw bandwidth, NVIDIA's superior compute/GPGPU efficiency took over and there were some coins that NVIDIA was 2x or more more efficient on per-watt and also the winner in absolute performance. (the thing to remember is that compute is the part that ASICs can do efficiently, and I always questioned whether those altcoins were really ASIC-resistant. But the ProgPOW-style algorithms doing better on NVIDIA cards never bothered/confused me, the reality is that most GPGPU programs "favored NVIDIA" during this era and Ethereum's proof-of-bandwidth model was an exception. Pascal was an efficiency beast and Polaris and Vega were only ok at best, outside their enormous, dangling memory buses.) This state of affairs persisted throughout the 5700 series until AMD launched the 6000 series, where they shifted to a design with smaller memory buses and more cache, which put them in the inverse situation of 2014-2015 where they were getting more out of a weaker memory subsystem than NVIDIA. And I think they did this on purpose because they wanted to "opt out" of the mining boom/bust cycle, and NVIDIA made a similar approach with Ada that has been extremely unpopular (despite AMD leading the way on this a few years before on their cards too). Isn't it wonderfully coincidental that out of any amount NVIDIA could have put into the LHR to slow down when it detected mining, that they put in the exact amount that dropped their cards to the same relative mining performance as AMD, and moved to the same cache-based approaches in the next generation as well? That has always been my take around LHR - it's not that they didn't like mining revenue, it's that they didn't want NVIDIA cards to be disproportionately pulled off shelves like happened to AMD in the 2014 and 2017 mining booms. People remember the "AMD was $1600, NVIDIA was $2400" situation already, they didn't want that to persist and turn into actual marketshare.
• deleted 3y ago
  [deleted]
• pjmlp 3y ago
  I bet NVidia is quite happy with the Switch deal. Secondly, the anti-CUDA alternatives all suck, as they keep heads down on their C first approach instead of being a polyglot ecosystem. SPIR, HIP and whatever else, lack the tooling, had a very cold embrace of C++ features, and little else, and the drivers, oh boy.
  • imtringued 3y ago
    The problem isn't C. The problem is that the buggy OpenCL garbage doesn't work at all. I once used hashcat with OpenCL in a security course on an AMD GPU and it made my system completely unstable. I couldn't care less what the kernel is written in. I'm never going to use hashcat on AMD GPUs ever again. The other problem is that companies invest in some alternative to OpenCL which fragments the non-CUDA ecosystem.
• two_in_one 3y ago
  > Nvidia failed mobile computing, failed embedded computing, Really? What do you think high-end drones and self-driving cars are using. They invested in generic robotics software and hardware probably more than all others put together. When you go beyond Raspberry Pi it's only NVidia. Their day is coming. Another similar ecosystem will be hard to create.
  • DeathArrow 3y ago
    > Really? What do you think high-end drones and self-driving cars are using. What are they using?
    • ptsneves 3y ago
      Nvidia Tegra[1]. It allows for machine vision and heavy maths to be executed very fast. There is basically nothing similar in an embedded format in the market. The remainder would be SoCs for Android phones (Samsung Exynos?). Thsese SoCs have lots of hacky drivers and mostly support Android, which is not a very good fit for real time and high customization required for drones and self driving cars (AOSP build system, a google class piece of crap). [1] https://en.wikipedia.org/wiki/Tegra https://en.wikipedia.org/wiki/Tegra
• thou4o2i34u234 3y ago
  Given how AMD/ATI has fared with their shitty software ecosystem after close to 20 years (the first paper on using GPUs for CNN was in '05/'06 -ish where they were using shaders), calling Nvidia 'lucky' is quite unfair. Those of us who were unlucky enough to buy a AMD GPU based on 'flop-count' were hurt quite badly. Nvidia's software compute-infra is simply unmatched.
  • mrguyorama 3y ago
    Having a competitor that seemingly refuses to compete is pretty damn lucky. There's not a single thing nVidia could have legally done to make that happen.
• DeathArrow 3y ago
  >then its parallel GPU got "lucky" You can only profit from luck if you prepare for it.
• Gordonjcp 3y ago
  Actually, no. NVidia has always been the first choice for graphics work, and especially now with large complex VFX pipelines there really isn't an alternative - particularly since most of that work is done on Linux which has always had excellent support from NVidia.
• esjeon 3y ago
  It's not pure luck tho. The GPGPU research started in early 2000s IIRC. Nvidia had invested into it earlier than anyone and more than anyone. That's how they got CUDA. Nvidia was ready for the next generation computing. It's just that no one, including Nvidia, knew when it's gonna hit the market.
  • pjmlp 3y ago
    In consumer graphics cards yes, compute in graphics chips is as old as the games industry, see chips like TMS34010.