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If that 2x price/performance scales for all of Google's inferencing then it is definitely not a loss for them. If they can halve their running costs for inferen

If that 2x price/performance scales for all of Google's inferencing then it is definitely not a loss for them. If they can halve their running costs for inferencing then they are saving themselves a ton of money. Their TPUv2 was announced slightly before the V100 and the money savings they make by not paying Nvidia premiums probably helps. From the customer point of view, what is a GPU other than a specialised accelerator. Without more details we can't know how a TPU really compares, but if your aim is to train/run inference of Tensorflow models, then they're a really competitive product at the moment.

smallnamespace 8y ago
I agree, but chip development is an expensive business. There is nothing preventing Nvidia from immediately turning around and building a specialised ML accelerator with better software integration and higher bandwidth. For all we know they could already be working on one.
- jacksmith21006 8y ago
  They already did two generations. Google has over $100B in the bank with less than $4B debt. So money is not an issue. It is tiny in the scheme of things. Google has an advantage as they do the entire stack and can better optimize like we see here with half the cost.
  - smallnamespace 8y ago
    Nvidia is actively building an entire deep learning stack internally, all the way to releasing a self-driving simulation platform which they are using to build their own self-driving software [1]. I think they are actually farther along and more aggressive about exploring deep learning use cases in production than Google today; augmenting real data with extensive simulation is really a far-reaching idea that comes directly from their gaming experience. > So money is not an issue. It is tiny in the scheme of things. Money of course is always an issue long term; otherwise why doesn't Google Fiber just spend tens of billions of dollars to build out its nationwide network? Because it will see negative ROI even if they succeed. The TPU has to eventually make a real return to Google, and it won't if nvidia can spend the same amount of money and build a faster product and sell it to all the other cloud players, which I believe they definitely can. Put another way, the TPU has to be cheaper to Google than buying nvidia GPUs after factoring in its development costs, whereas nvidia gets to amortize those dev costs over all other cloud providers and all other GPU customers. Google isn't about to sell the TPU to other cloud providers; the entire idea is to use it to drive Google Cloud adoption. The TPU is a fine chip, but if you just look at the big picture, there is every sign that nvidia could build the same or better product for less money because it has far more synergies across the hardware and chip design stack; e.g. the TPU only has PCIe connectors, while nvidia has already worked with IBM to get NVLink into supercomputers [2]. For some workloads the TPU will likely be bandwidth-starved communicating with the CPU and main memory. [1] https://nvidianews.nvidia.com/news/nvidia-introduces-drive-constellation-simulation-system-to-safely-drive-autonomous-vehicles-billions-of-miles-in-virtual-reality https://nvidianews.nvidia.com/news/nvidia-introduces-drive-c... [2] https://www.ibm.com/us-en/marketplace/power-systems-ac922/details https://www.ibm.com/us-en/marketplace/power-systems-ac922/de...
    - jacksmith21006 8y ago
      The problem is Nvidia is never going to have the AI expertise up and down the stack like Google. As far as I am aware Nvidia does not even run a cloud do they? Obviously never going to have the production NN that Google has. Google now has well over 4k NN in production and not sure if Nvidia has any? Well over a billion a day are using the Google NN. That data allows Google to iterate in ways that Nvidia just never would be able to. But this was all theory and why starting to see a little more concrete results like this where Google with their TPUs able to charge 1/2 the price of using Nvidia is value. Then we also have the paper from Google on the Gen 1. I would guess Google is working on a gen 3. Nvidia is trying to catch a moving target but without the data. So they are behind, trying to catch up, but missing an arm. A perfect example of this phenomenon is Capsule network pioneered by Hinton. They use dynamic routing which is potentially going to require different approach to memory access as the pattern would be different than CNN or RNN. Today the problem is memory access and no longer instruction execution. Google nailed the low hanging fruit with the Gen 1 TPUs. They have 65536 very simple cores. Now you have to go after memory access. Your post is all over the place so a bit hard to respond. Google Fiber was NOT about cost. It was about AT&T and other established players with some local governments making it difficult for Google to access what they needed to be able to compete. I hate debating something with someone that is doing what you are doing. Google Fiber? Really? "I think they are actually farther along and more aggressive about exploring deep learning" I do a LOT of surfing on sites and can easily say this is the craziest thing I have read in a bit. You are honestly comparing Nvidia to Google? Really? Google solved Go a decade early. Hinton did the Capsule networks and basically the farther of DL. Well made it actually work. What breakthrough came from Nvidia? A single one? There is so much crazy stuff in your posts this must be driven by something else and something emotional? Your points are just not based on reality. Is this really about Google firing Damore? BTW, Nvidia read the Google Gen 1 TPU paper and why we see them doing similar things. But Google is going to move to addressing the memory access problems as that is the next area to improve. Once Google figures it out then you will see Nvidia just copy the approach like they are doing with the gen 1 TPUs. I listened to this Nvidia presentation on YouTube and they were basically quoting the Google TPU paper. Talking about using 8 bit, integers, etc, for inference. Google will release the gen 3 and then share a paper on the gen 2 and we will see Nvivida then try to copy that one. Nvidia always a couple of steps behind. But I am a super curious person and can you share what this is really all about?

smallnamespace 8y ago

I agree, but chip development is an expensive business. There is nothing preventing Nvidia from immediately turning around and building a specialised ML accelerator with better software integration and higher bandwidth. For all we know they could already be working on one.

jacksmith21006 8y ago
They already did two generations. Google has over $100B in the bank with less than $4B debt. So money is not an issue. It is tiny in the scheme of things. Google has an advantage as they do the entire stack and can better optimize like we see here with half the cost.
- smallnamespace 8y ago
  Nvidia is actively building an entire deep learning stack internally, all the way to releasing a self-driving simulation platform which they are using to build their own self-driving software [1]. I think they are actually farther along and more aggressive about exploring deep learning use cases in production than Google today; augmenting real data with extensive simulation is really a far-reaching idea that comes directly from their gaming experience. > So money is not an issue. It is tiny in the scheme of things. Money of course is always an issue long term; otherwise why doesn't Google Fiber just spend tens of billions of dollars to build out its nationwide network? Because it will see negative ROI even if they succeed. The TPU has to eventually make a real return to Google, and it won't if nvidia can spend the same amount of money and build a faster product and sell it to all the other cloud players, which I believe they definitely can. Put another way, the TPU has to be cheaper to Google than buying nvidia GPUs after factoring in its development costs, whereas nvidia gets to amortize those dev costs over all other cloud providers and all other GPU customers. Google isn't about to sell the TPU to other cloud providers; the entire idea is to use it to drive Google Cloud adoption. The TPU is a fine chip, but if you just look at the big picture, there is every sign that nvidia could build the same or better product for less money because it has far more synergies across the hardware and chip design stack; e.g. the TPU only has PCIe connectors, while nvidia has already worked with IBM to get NVLink into supercomputers [2]. For some workloads the TPU will likely be bandwidth-starved communicating with the CPU and main memory. [1] https://nvidianews.nvidia.com/news/nvidia-introduces-drive-constellation-simulation-system-to-safely-drive-autonomous-vehicles-billions-of-miles-in-virtual-reality https://nvidianews.nvidia.com/news/nvidia-introduces-drive-c... [2] https://www.ibm.com/us-en/marketplace/power-systems-ac922/details https://www.ibm.com/us-en/marketplace/power-systems-ac922/de...
  - jacksmith21006 8y ago
    The problem is Nvidia is never going to have the AI expertise up and down the stack like Google. As far as I am aware Nvidia does not even run a cloud do they? Obviously never going to have the production NN that Google has. Google now has well over 4k NN in production and not sure if Nvidia has any? Well over a billion a day are using the Google NN. That data allows Google to iterate in ways that Nvidia just never would be able to. But this was all theory and why starting to see a little more concrete results like this where Google with their TPUs able to charge 1/2 the price of using Nvidia is value. Then we also have the paper from Google on the Gen 1. I would guess Google is working on a gen 3. Nvidia is trying to catch a moving target but without the data. So they are behind, trying to catch up, but missing an arm. A perfect example of this phenomenon is Capsule network pioneered by Hinton. They use dynamic routing which is potentially going to require different approach to memory access as the pattern would be different than CNN or RNN. Today the problem is memory access and no longer instruction execution. Google nailed the low hanging fruit with the Gen 1 TPUs. They have 65536 very simple cores. Now you have to go after memory access. Your post is all over the place so a bit hard to respond. Google Fiber was NOT about cost. It was about AT&T and other established players with some local governments making it difficult for Google to access what they needed to be able to compete. I hate debating something with someone that is doing what you are doing. Google Fiber? Really? "I think they are actually farther along and more aggressive about exploring deep learning" I do a LOT of surfing on sites and can easily say this is the craziest thing I have read in a bit. You are honestly comparing Nvidia to Google? Really? Google solved Go a decade early. Hinton did the Capsule networks and basically the farther of DL. Well made it actually work. What breakthrough came from Nvidia? A single one? There is so much crazy stuff in your posts this must be driven by something else and something emotional? Your points are just not based on reality. Is this really about Google firing Damore? BTW, Nvidia read the Google Gen 1 TPU paper and why we see them doing similar things. But Google is going to move to addressing the memory access problems as that is the next area to improve. Once Google figures it out then you will see Nvidia just copy the approach like they are doing with the gen 1 TPUs. I listened to this Nvidia presentation on YouTube and they were basically quoting the Google TPU paper. Talking about using 8 bit, integers, etc, for inference. Google will release the gen 3 and then share a paper on the gen 2 and we will see Nvivida then try to copy that one. Nvidia always a couple of steps behind. But I am a super curious person and can you share what this is really all about?