4 ms·

Yes. In addition to Nvidia and Google it will also have to compete with Intel, Cerebras, SambaNova, Graphcore, and maybe even AMD.

by p1esk 4y ago

TaylorAlexander 4y ago
The point was that very few companies can pull off a clean sheet chip design from nothing. Google has done it, but Google is an elite company. So saying Tesla isn't the only company that has done it because Google has done it only shows that Tesla is doing very well. But it seems clear to me that the point isn't to compete with these other companies, but to vertically integrate a critical component of their systems. They can discard a lot of legacy concerns and focus on raw power. The thing is that if Tesla just uses Nvidia, like everyone else, then Tesla's stuff is only differentiated by software. Everyone uses Nvidia and it becomes hard to set themselves apart. But if Nvidia is chasing a broad customer base and has all this extra stuff to think about, then Tesla could potentially be more nimble, and produce a holistic system design that solves exactly the problems they have with no chuff. This could result in a more advanced hardware platform so their robotics products differentiate themselves with both hardware and software. I am also happy because I am a robotics engineer, and in my opinion we need hardware that is 1000x more powerful than today to do what we really need. Nvidia wants to move at a certain pace, but if Tesla is trying to beat them on raw power, then Nvidia will play catch up and accelerate development of more powerful systems. This is great for everyone.
- p1esk 4y ago
  Internally Tesla can use whatever they want and it might even make sense in the long term, but if they want to sell their chips for general model training they better be much better than the future Nvidia cards they will be competing with when they start selling. Like twice faster at half the price and half the power - with perfect framework support. That last part is extremely important: if I see any mentions of anyone who changed “cuda” to “dojo” in their Pytorch code and ran into any issues, I’m not going to touch it with a ten foot pole. Just like I avoid TPUs because 2 years ago I heard people were having issues with them. And I’m the guy who has decided which hardware to spend millions of dollars on at several companies.
  - TaylorAlexander 4y ago
    Yeah I just don't think that is really the main part of their strategy. Maybe they would sell chips or boards or servers if they are already making them, but I think it is mostly about internal use so their end products have a competitive advantage as complete robots. Robotics needs HUGE advances in compute and with their own chips Tesla won't have to be dependent on a third party for their success. All the stuff you talked about about needing perfect support before you will touch it is something that takes a lot of work for nvidia and others, slowing them down. Tesla can ignore all that and focus on performance for their specific application, and I think this gives them the freedom to lead the pack on raw performance for their application. I'm not sure if you've watched the presentations for how their self driving system is trained, but basically they have a million vehicles out in the real world with camera systems on them, and they have a massive server farm that is collecting new data from vehicles all the time, and they train their neural net by running millions of scenarios in simulation and against real world data collected from all those vehicles. And they have to re-train the system all the time with new data, and run it against old data to check for regressions. So they have this huge compute requirement to keep improving their system. They think that functional self driving will revolutionize their business (setting aside the valid criticism, this is what Tesla thinks) so they need to be able to handle an ever growing compute load that they have to be running constantly. So raw compute power is critical to the success of their plan. It may not be enough, but if they certainly can't succeed without it. But their needs are very specific, and it sounds like they've found an architecture which is simpler than most nvidia chips, but has loads of power. So it sounds like they are making a good decision, based on their specific needs. It is a huge risky bet, but then that's how Musk likes to do things.
    - p1esk 4y ago
      Robotics needs HUGE advances in compute This is surprising to me. Robotics clearly needs huge advances in algorithms (RL or something better). Do you mean you need faster hardware to discover those algorithms?
      - TaylorAlexander 4y ago

Yes. In addition to Nvidia and Google it will also have to compete with Intel, Cerebras, SambaNova, Graphcore, and maybe even AMD.

by p1esk 4y ago

Yes. In addition to Nvidia and Google it will also have to compete with Intel, Cerebras, SambaNova, Graphcore, and maybe even AMD.

TaylorAlexander 4y ago

The point was that very few companies can pull off a clean sheet chip design from nothing. Google has done it, but Google is an elite company. So saying Tesla isn't the only company that has done it because Google has done it only shows that Tesla is doing very well. But it seems clear to me that the point isn't to compete with these other companies, but to vertically integrate a critical component of their systems. They can discard a lot of legacy concerns and focus on raw power. The thing is that if Tesla just uses Nvidia, like everyone else, then Tesla's stuff is only differentiated by software. Everyone uses Nvidia and it becomes hard to set themselves apart. But if Nvidia is chasing a broad customer base and has all this extra stuff to think about, then Tesla could potentially be more nimble, and produce a holistic system design that solves exactly the problems they have with no chuff. This could result in a more advanced hardware platform so their robotics products differentiate themselves with both hardware and software. I am also happy because I am a robotics engineer, and in my opinion we need hardware that is 1000x more powerful than today to do what we really need. Nvidia wants to move at a certain pace, but if Tesla is trying to beat them on raw power, then Nvidia will play catch up and accelerate development of more powerful systems. This is great for everyone.

p1esk 4y ago
Internally Tesla can use whatever they want and it might even make sense in the long term, but if they want to sell their chips for general model training they better be much better than the future Nvidia cards they will be competing with when they start selling. Like twice faster at half the price and half the power - with perfect framework support. That last part is extremely important: if I see any mentions of anyone who changed “cuda” to “dojo” in their Pytorch code and ran into any issues, I’m not going to touch it with a ten foot pole. Just like I avoid TPUs because 2 years ago I heard people were having issues with them. And I’m the guy who has decided which hardware to spend millions of dollars on at several companies.
- TaylorAlexander 4y ago
  Yeah I just don't think that is really the main part of their strategy. Maybe they would sell chips or boards or servers if they are already making them, but I think it is mostly about internal use so their end products have a competitive advantage as complete robots. Robotics needs HUGE advances in compute and with their own chips Tesla won't have to be dependent on a third party for their success. All the stuff you talked about about needing perfect support before you will touch it is something that takes a lot of work for nvidia and others, slowing them down. Tesla can ignore all that and focus on performance for their specific application, and I think this gives them the freedom to lead the pack on raw performance for their application. I'm not sure if you've watched the presentations for how their self driving system is trained, but basically they have a million vehicles out in the real world with camera systems on them, and they have a massive server farm that is collecting new data from vehicles all the time, and they train their neural net by running millions of scenarios in simulation and against real world data collected from all those vehicles. And they have to re-train the system all the time with new data, and run it against old data to check for regressions. So they have this huge compute requirement to keep improving their system. They think that functional self driving will revolutionize their business (setting aside the valid criticism, this is what Tesla thinks) so they need to be able to handle an ever growing compute load that they have to be running constantly. So raw compute power is critical to the success of their plan. It may not be enough, but if they certainly can't succeed without it. But their needs are very specific, and it sounds like they've found an architecture which is simpler than most nvidia chips, but has loads of power. So it sounds like they are making a good decision, based on their specific needs. It is a huge risky bet, but then that's how Musk likes to do things.
  - p1esk 4y ago
    Robotics needs HUGE advances in compute This is surprising to me. Robotics clearly needs huge advances in algorithms (RL or something better). Do you mean you need faster hardware to discover those algorithms?
    - TaylorAlexander 4y ago