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> The latest from NVIDIA requires liquid cooling, tons of power, and solid floors We won't be running on GPUs forever. The innovation in the chip space will co

> The latest from NVIDIA requires liquid cooling, tons of power, and solid floors We won't be running on GPUs forever. The innovation in the chip space will continue. With as much margin as cloud takes, there will be increased interest in on-prem and mixed environments. We run our own GPUs in production as well as having a cloud footprint.

jedberg 3y ago
> We won't be running on GPUs forever. The innovation in the chip space will continue. Sure, but I'm seeing a divergence. There are a group of chips that are getting smaller and require less power and cooling that can do a lot of workloads that you used to need huge chips for, but there are the most advanced chips for the most advanced workloads getting bigger and more power hungry. There will always be the big customers who need the big chips, and that is what will be nearly impossible for them to self host.
- PLenz 3y ago
  This is the same argument that mainframe companies made in the 80s. Mass commodity systems will eventually kill the big gpu except for legacy and niche workloads. It's the tech infra circle of life.
  - cogman10 3y ago
    I'm less optimistic. Unlike the 80s we are quickly approaching the limits of node feature sizes. Barring radically new designs and materials, I think it's reasonable to assume that we are reaching the power:computational limits of silicon.
    - latchkey 3y ago
      Maybe in the case of CPUs. But, there is a fundamental technical shift going on away from computationally expensive things being done on CPUs and moving to AIAs. I suspect that this is why Jensen said Moore's law is dead.
      - refulgentis 3y ago
        Jensen said Moore's Law is dead because of the dichotomy explained a few times -- the free lunches are coming fewer and far between, you can't get a substantial performance boost and power decrease every 18 months, per Moore's Law. We are reaching the power:computational limits of silicon. Both GPU and CPU.
        latchkey 3y ago
        Here is the quote: “Moore’s Law’s dead,” Huang said, referring to the standard that the number of transistors on a chip doubles every two years. “And the ability for Moore’s Law to deliver twice the performance at the same cost, or at the same performance, half the cost, every year and a half, is over. It’s completely over, and so the idea that a chip is going to go down in cost over time, unfortunately, is a story of the past. Computing is a not a chip problem, it’s a software and chip problem,” Huang said. What we are seeing now are software engineers offloading computationally expensive workloads to AIA's, more and more. This is enabled through the use of libraries like PyTorch and access to HPC levels of compute that were not broadly accessible before.
        MichaelZuo 3y ago
        The 'AIA's also have a scaling limit, and the coordination overhead increases exponentially too. e.g. A million chickens could have a thousand times more muscle mass than two strong oxen, but I doubt anyone could plow a field even 2x faster with a million chickens. So it's only a net benefit when the work can be split up into tiny parallel chunks and then recombined with near perfect efficiently.
        latchkey 3y ago
        > So it's only a net benefit when the work can be split up into tiny parallel chunks and then recombined with near perfect efficiently. Hasn't that pretty much always been the case? One thing we are seeing more and more of is composable fabrics where the PCIe bus is effectively being extended outside the case such that you log into a single instance, and instead of seeing just 8 AIA's, you now see 32+. This makes the coordination a lot easier.
  - fragmede 3y ago
    What's different today is the divide between training and inference. Inference is ridiculously cheap compared to training, and we're still early days with optimizations across the whole of the stack, so we'll have to see how it develops. Once constant training gets figured out, then we're really in for a ride.
marcosdumay 3y ago
> With as much margin as cloud takes, there will be increased interest in on-prem and mixed environments. Or cheaper clouds... I wonder how much lock-in the current clouds actually have. And if they'll fully take customers down or entire industries hostage like the mainframes did.
- latchkey 3y ago
  GPU clouds use long term reserved cloud contracts as lock-in. Lambda started off kind of bragging about shorter term contracts on their website and then changed that to one year after the demand picked up (and investors piled in tons of money). https://lambdalabs.com/blog/voltron-data-case-study-why-ml-teams-using-reserved-cloud-clusters https://lambdalabs.com/blog/voltron-data-case-study-why-ml-t... "While most other cloud companies offer contracts starting at one year, you can reserve a Lambda Reserved Cloud Cluster starting at six months."
latchkey 3y ago
GPUs are no longer GPUs... ai accelerators (AIA) is a better term. My MI300x don't have display ports. ;-) I actually see a lot of parallels to bitcoin mining. It started on cpus, then gpus, then fpgas and soon after asics. We are starting to see something similar in the AI space with groq and tenstorrent. The problem with asic's today is that in order to get to the same compute density of a AIA, due to AI's need for memory, you need significantly more hardware. Right now, MI300x is the leader with 192gb, if you do the math on groq, it just doesn't work out. This is part of the reason why the stopped selling to retail and are just focused on hosting services. It will be a while before the smaller chipmakers can get their hands on enough hbm to make a dent in the AIA market (due to AMD/NV hogging it all). Don't forget that AMD/NV can and will just come out with better designs too.
nradov 3y ago
The demand for compute utility capacity is effectively infinite. Eventually we'll move on from GPUs to some new architecture but the servers will still have to run in data centers that can provide enough power and cooling.

jedberg 3y ago

> We won't be running on GPUs forever. The innovation in the chip space will continue. Sure, but I'm seeing a divergence. There are a group of chips that are getting smaller and require less power and cooling that can do a lot of workloads that you used to need huge chips for, but there are the most advanced chips for the most advanced workloads getting bigger and more power hungry. There will always be the big customers who need the big chips, and that is what will be nearly impossible for them to self host.

PLenz 3y ago
This is the same argument that mainframe companies made in the 80s. Mass commodity systems will eventually kill the big gpu except for legacy and niche workloads. It's the tech infra circle of life.
- cogman10 3y ago
  I'm less optimistic. Unlike the 80s we are quickly approaching the limits of node feature sizes. Barring radically new designs and materials, I think it's reasonable to assume that we are reaching the power:computational limits of silicon.
  - latchkey 3y ago
    Maybe in the case of CPUs. But, there is a fundamental technical shift going on away from computationally expensive things being done on CPUs and moving to AIAs. I suspect that this is why Jensen said Moore's law is dead.
    - refulgentis 3y ago
      Jensen said Moore's Law is dead because of the dichotomy explained a few times -- the free lunches are coming fewer and far between, you can't get a substantial performance boost and power decrease every 18 months, per Moore's Law. We are reaching the power:computational limits of silicon. Both GPU and CPU.
      - latchkey 3y ago
        Here is the quote: “Moore’s Law’s dead,” Huang said, referring to the standard that the number of transistors on a chip doubles every two years. “And the ability for Moore’s Law to deliver twice the performance at the same cost, or at the same performance, half the cost, every year and a half, is over. It’s completely over, and so the idea that a chip is going to go down in cost over time, unfortunately, is a story of the past. Computing is a not a chip problem, it’s a software and chip problem,” Huang said. What we are seeing now are software engineers offloading computationally expensive workloads to AIA's, more and more. This is enabled through the use of libraries like PyTorch and access to HPC levels of compute that were not broadly accessible before.
        MichaelZuo 3y ago
        The 'AIA's also have a scaling limit, and the coordination overhead increases exponentially too. e.g. A million chickens could have a thousand times more muscle mass than two strong oxen, but I doubt anyone could plow a field even 2x faster with a million chickens. So it's only a net benefit when the work can be split up into tiny parallel chunks and then recombined with near perfect efficiently.
        latchkey 3y ago
        > So it's only a net benefit when the work can be split up into tiny parallel chunks and then recombined with near perfect efficiently. Hasn't that pretty much always been the case? One thing we are seeing more and more of is composable fabrics where the PCIe bus is effectively being extended outside the case such that you log into a single instance, and instead of seeing just 8 AIA's, you now see 32+. This makes the coordination a lot easier.
- fragmede 3y ago
  What's different today is the divide between training and inference. Inference is ridiculously cheap compared to training, and we're still early days with optimizations across the whole of the stack, so we'll have to see how it develops. Once constant training gets figured out, then we're really in for a ride.

marcosdumay 3y ago

> With as much margin as cloud takes, there will be increased interest in on-prem and mixed environments. Or cheaper clouds... I wonder how much lock-in the current clouds actually have. And if they'll fully take customers down or entire industries hostage like the mainframes did.

latchkey 3y ago
GPU clouds use long term reserved cloud contracts as lock-in. Lambda started off kind of bragging about shorter term contracts on their website and then changed that to one year after the demand picked up (and investors piled in tons of money). https://lambdalabs.com/blog/voltron-data-case-study-why-ml-teams-using-reserved-cloud-clusters https://lambdalabs.com/blog/voltron-data-case-study-why-ml-t... "While most other cloud companies offer contracts starting at one year, you can reserve a Lambda Reserved Cloud Cluster starting at six months."

latchkey 3y ago

GPUs are no longer GPUs... ai accelerators (AIA) is a better term. My MI300x don't have display ports. ;-) I actually see a lot of parallels to bitcoin mining. It started on cpus, then gpus, then fpgas and soon after asics. We are starting to see something similar in the AI space with groq and tenstorrent. The problem with asic's today is that in order to get to the same compute density of a AIA, due to AI's need for memory, you need significantly more hardware. Right now, MI300x is the leader with 192gb, if you do the math on groq, it just doesn't work out. This is part of the reason why the stopped selling to retail and are just focused on hosting services. It will be a while before the smaller chipmakers can get their hands on enough hbm to make a dent in the AIA market (due to AMD/NV hogging it all). Don't forget that AMD/NV can and will just come out with better designs too.

nradov 3y ago

The demand for compute utility capacity is effectively infinite. Eventually we'll move on from GPUs to some new architecture but the servers will still have to run in data centers that can provide enough power and cooling.