8 ms·

Nvidia smells a lot of profit and maximise it by forcing prosumers to buy trashy value "quadro" or even worse buying data center GPU's for working on state of t

Nvidia smells a lot of profit and maximise it by forcing prosumers to buy trashy value "quadro" or even worse buying data center GPU's for working on state of the art problems (6-20k usd minimum investment to do normal sized Deep Learning in next 2/3 years incoming and probably 3/4x that for training). Big fuck you for nvidia with this release prosumer market is dying right now and there is no alternative cause AMD software is so bad you are forced to use CUDA in for example DL/DS.

mort96 4y ago
I don't understand why the ML community went so hard in on CUDA. Maybe using nvidia cards made sense back around pascal, but I don't get why they would choose to lock themselves into nvidia's proprietary APIs. You reap what you sow.
- binarymax 4y ago
  Because CUDA was a fantastic API from the beginning, and it took forever for OpenCL to catch up (has it even really caught up?). I remember writing CUDA parallelized C++ code in 2010 and it was a piece of cake.
  - mort96 4y ago
    Alright, in that case I suppose the trade-off was clear: use a nice API and lock yourself in, or use a less nice API and be able to use any hardware. Nvidia cards becoming less competitive over time is in any case a very predictable outcome. Sounds like the ML community made a trade-off, and this is the downside of that trade-off.
    - meragrin_ 4y ago
      > Sounds like the ML community made a trade-off Nope, AMD never gave them a choice.
  - binarymax 4y ago
    @mort96 I've reached the thread depth and can't reply directly - but it's not just a case of a nice API. I don't even think it was possible to use alternative hardware and SDKs for consumer GPGPUs for deep learning when the field was taking off. It was not so much a choice but the only viable option. Has Nvidia taken advantage of their position? Absolutely. But don't fault the ML community when there was nothing else available.
    - mort96 4y ago
      HN limits how fast you can reply. I think it's to reduce flame wars and force people to take some time to cool off if things get heated :) Just wait a few minutes and it lets you respond. OpenCL has existed for about as long as CUDA, and can be used on GPUs from any of the major manufacturers. What makes OpenCL so unsuitable for ML that the ML community just had to use CUDA?
      - fny 4y ago
        CUDA came first and OpenCL tends to perform worse.
      - latency-guy2 4y ago
        CUDA was just plain better back in the day, more feature robust, and more features in general. Much simpler to pick up as well, especially at the time with what tools and libraries we were forced to use NVIDIA also partnered up very quickly with many big players in the game, the sales people went to work, but they had the technological feats to back it up. After that, it's the network effect.
      - jjoonathan 4y ago
        AMD's OpenCL implementation and tooling were really, really rough. Lots of hard lockups, memory leaks, and forum threads ending in the person with the question giving up and switching to team green. I followed this path eventually, too. The breaking point happened when I had spent an entire day trying to get a bit of OpenCL working, thinking that I was at fault, but then I tried running it on a NVidia box, hoping to get a more descriptive error, but the code Just Worked. NVidia's OpenCL implementation was better than AMD's, and not by a small amount. CUDA was better still. I realized I had thrown away $n0000 of my own time chasing a $n00 discount on the AMD card (controlling for perf). Never again. Now that AMD has money, hopefully they have fixed their stack, but I'm still in "once bitten, twice shy" mode. I want to see someone else in my field using AMD on tasks I care about before I try team red again.
- mistymountains 4y ago
  There are other options, including TPUs. Besides, cloud computing is what any sensible practitioner uses, despite how building a home rig is a fun hobby.
  - binarymax 4y ago
    I agree that most teams just use cloud compute. But if you're a startup that wants to not burn crazy amounts of money, then a home/office rig is definitely the way to go. A month or two worth of training in the cloud will cost the same as a V100.
    - uup 4y ago
      Yea, large companies that don't really care about operational spending will happily use GPUs. But for anyone else, spending a few thousand on GPUs will save a lot of money very quickly.
- etiam 4y ago
  It worked in practice at all at the time when there was a huge unmet need. Pretty much as simple as that, I'd say.
- Dylan16807 4y ago
  Well AMD can't even be bothered to get ROCm working on most of their cards so there seems to be a lot of self-inflicted trouble here.
  - carlmr 4y ago
    This I still don't understand. The path for AMD here would be clear if they wanted a piece of the cake.
    - dotnet00 4y ago
      I think that they've given up on competing directly for consumer level compute. It makes up a small (albeit important, since it's how you get developers in the first place) part of the market and their main userbase seems more than willing to overlook the lack of productivity features because the (predominantly gamer) users misguidedly think those are only for workstation/server grade hardware. They may be hoping to live off gamers and big server contracts (eg Frontier) until they get their software stack in a decent position and outgrow their current reputation of having buggy software.
- Narann 4y ago
  There are literally nothing reaching the knees of all you can do with CUDA. Peoples forgot how Nvidia can be good by creating and expanding it's own market just by providing quality softwares/API. On a side note, AMD did a different (yet interesting) move by embracing the FOSS culture (working with uptreams).
- varelse 4y ago
- bitL 4y ago
  Nvidia basically created the whole AI-on-GPU market, so it's not like there was any other option for like 10 years... OpenCL came as a hindered afterthought that is still strictly inferior to CUDA.
uup 4y ago
This card is expected to be around $6,000. Maybe it's just me, but I don't consider it that unreasonable. A lot of ML scientists are (were?) earning around 7-figures. Companies paying these salaries can afford a a $6,000 GPU. Of course, it'd be nice if there was a little price discrimination for the rest of us. Still, $6,000 doesn't seem too bad for cutting edge tech.
- machinekob 4y ago
  Sorry here in EU we are making a lot less past 2 years we have big increase in salary but still avg for EU is probably about ~60-80k USD if you have 4/5+ years of exp and if you are very good you can get 100-120k USD+ (not counting Meta/Google and few other but only in few countries) and ofc it is pre-tax as taxation is a lot higher than US so in some countries you'll get about 40-60% total salary (counting VAT on basic products and other taxation). Not even counting Eastern Europe and Asia where you get fraction of that.
  - bitL 4y ago
    Imagine spending 10%+ of your yearly income after taxation on this card...
    - machinekob 4y ago
      Imagine you have to spend 15% of your yearly salary every 2 years cause nvidia tax (ofc in western developer countries and more like 50% excluding cost of living in less fortunate areas)
      - uup 4y ago
    - 6nf 4y ago
      If it makes you 11% more productive then its worth it
  - TillE 4y ago

mort96 4y ago

I don't understand why the ML community went so hard in on CUDA. Maybe using nvidia cards made sense back around pascal, but I don't get why they would choose to lock themselves into nvidia's proprietary APIs. You reap what you sow.

binarymax 4y ago
Because CUDA was a fantastic API from the beginning, and it took forever for OpenCL to catch up (has it even really caught up?). I remember writing CUDA parallelized C++ code in 2010 and it was a piece of cake.
- mort96 4y ago
  Alright, in that case I suppose the trade-off was clear: use a nice API and lock yourself in, or use a less nice API and be able to use any hardware. Nvidia cards becoming less competitive over time is in any case a very predictable outcome. Sounds like the ML community made a trade-off, and this is the downside of that trade-off.
  - meragrin_ 4y ago
    > Sounds like the ML community made a trade-off Nope, AMD never gave them a choice.
- binarymax 4y ago
  @mort96 I've reached the thread depth and can't reply directly - but it's not just a case of a nice API. I don't even think it was possible to use alternative hardware and SDKs for consumer GPGPUs for deep learning when the field was taking off. It was not so much a choice but the only viable option. Has Nvidia taken advantage of their position? Absolutely. But don't fault the ML community when there was nothing else available.
  - mort96 4y ago
    HN limits how fast you can reply. I think it's to reduce flame wars and force people to take some time to cool off if things get heated :) Just wait a few minutes and it lets you respond. OpenCL has existed for about as long as CUDA, and can be used on GPUs from any of the major manufacturers. What makes OpenCL so unsuitable for ML that the ML community just had to use CUDA?
    - fny 4y ago
      CUDA came first and OpenCL tends to perform worse.
    - latency-guy2 4y ago
      CUDA was just plain better back in the day, more feature robust, and more features in general. Much simpler to pick up as well, especially at the time with what tools and libraries we were forced to use NVIDIA also partnered up very quickly with many big players in the game, the sales people went to work, but they had the technological feats to back it up. After that, it's the network effect.
    - jjoonathan 4y ago
      AMD's OpenCL implementation and tooling were really, really rough. Lots of hard lockups, memory leaks, and forum threads ending in the person with the question giving up and switching to team green. I followed this path eventually, too. The breaking point happened when I had spent an entire day trying to get a bit of OpenCL working, thinking that I was at fault, but then I tried running it on a NVidia box, hoping to get a more descriptive error, but the code Just Worked. NVidia's OpenCL implementation was better than AMD's, and not by a small amount. CUDA was better still. I realized I had thrown away $n0000 of my own time chasing a $n00 discount on the AMD card (controlling for perf). Never again. Now that AMD has money, hopefully they have fixed their stack, but I'm still in "once bitten, twice shy" mode. I want to see someone else in my field using AMD on tasks I care about before I try team red again.
mistymountains 4y ago
There are other options, including TPUs. Besides, cloud computing is what any sensible practitioner uses, despite how building a home rig is a fun hobby.
- binarymax 4y ago
  I agree that most teams just use cloud compute. But if you're a startup that wants to not burn crazy amounts of money, then a home/office rig is definitely the way to go. A month or two worth of training in the cloud will cost the same as a V100.
  - uup 4y ago
    Yea, large companies that don't really care about operational spending will happily use GPUs. But for anyone else, spending a few thousand on GPUs will save a lot of money very quickly.
etiam 4y ago
It worked in practice at all at the time when there was a huge unmet need. Pretty much as simple as that, I'd say.
Dylan16807 4y ago
Well AMD can't even be bothered to get ROCm working on most of their cards so there seems to be a lot of self-inflicted trouble here.
- carlmr 4y ago
  This I still don't understand. The path for AMD here would be clear if they wanted a piece of the cake.
  - dotnet00 4y ago
    I think that they've given up on competing directly for consumer level compute. It makes up a small (albeit important, since it's how you get developers in the first place) part of the market and their main userbase seems more than willing to overlook the lack of productivity features because the (predominantly gamer) users misguidedly think those are only for workstation/server grade hardware. They may be hoping to live off gamers and big server contracts (eg Frontier) until they get their software stack in a decent position and outgrow their current reputation of having buggy software.
Narann 4y ago
There are literally nothing reaching the knees of all you can do with CUDA. Peoples forgot how Nvidia can be good by creating and expanding it's own market just by providing quality softwares/API. On a side note, AMD did a different (yet interesting) move by embracing the FOSS culture (working with uptreams).
varelse 4y ago
bitL 4y ago
Nvidia basically created the whole AI-on-GPU market, so it's not like there was any other option for like 10 years... OpenCL came as a hindered afterthought that is still strictly inferior to CUDA.

uup 4y ago

This card is expected to be around $6,000. Maybe it's just me, but I don't consider it that unreasonable. A lot of ML scientists are (were?) earning around 7-figures. Companies paying these salaries can afford a a $6,000 GPU. Of course, it'd be nice if there was a little price discrimination for the rest of us. Still, $6,000 doesn't seem too bad for cutting edge tech.

machinekob 4y ago
Sorry here in EU we are making a lot less past 2 years we have big increase in salary but still avg for EU is probably about ~60-80k USD if you have 4/5+ years of exp and if you are very good you can get 100-120k USD+ (not counting Meta/Google and few other but only in few countries) and ofc it is pre-tax as taxation is a lot higher than US so in some countries you'll get about 40-60% total salary (counting VAT on basic products and other taxation). Not even counting Eastern Europe and Asia where you get fraction of that.
- bitL 4y ago
  Imagine spending 10%+ of your yearly income after taxation on this card...
  - machinekob 4y ago
    Imagine you have to spend 15% of your yearly salary every 2 years cause nvidia tax (ofc in western developer countries and more like 50% excluding cost of living in less fortunate areas)
    - uup 4y ago
  - 6nf 4y ago
    If it makes you 11% more productive then its worth it
- TillE 4y ago