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What sort of transition, exactly? Their core business is intact and they're strongly positioned in AI and autonomous driving. It seems like you're blaming man

by danhak 8y ago

What sort of transition, exactly? Their core business is intact and they're strongly positioned in AI and autonomous driving. It seems like you're blaming management for the fact that the stock market mispriced the long-term value of crypto mining.

twblalock 8y ago
It's not like Nvidia was trying to hold down expectations and the market went crazy anyway. Nvidia based their own revenue guidance on the same assumptions about crypto mining that market analysts made. They missed their guidance in November because of that.
Nelson69 8y ago
Not just is it intact, but is there really an alternative ML/AI platform out there? They're still the best (only?) game in town with the full stack. And you can go buy or lease access to their parts right now and get radical performance improvements for your model training.
- LAMY2000_EF 8y ago
  kseems like a lot of startups are trying to build things equivalent to TPUs. Also one can use FPGAs for inference(?) maybe also training? I think a lot of people see an opportunity to try to take a slice of the big compute market for ML/AI. who knows if these alternatives really are viable.... but google has a lot of smart people and building your own silicon seems like a great idea these days..so I think other people will attack their stack.
  - sitkack 8y ago
    AI is a pretty tractable workload for GPUs/TPUs/FPGAs. Inference is moving to many more places, the days of Nvidia being the solution for AI are over. It will still be the goto for research, but as things mature, the nets will literally run anywhere there is a MAC (multiply accumulate).
    - pmoriarty 8y ago
      It may run, but will it run as cost-effectively for the same amount of performance as it would on a GPU?
      - Setepenre 8y ago
        yeah, you have GPU like chips being developed by a lot of companies invested in AI. From server chips for training, down to cell phone chips for cheap inference. I am really hoping to see AMD entering the battlefield. AMD GPUs are more than capable of handling the workload it is just a software problem.

by danhak 8y ago

twblalock 8y ago

It's not like Nvidia was trying to hold down expectations and the market went crazy anyway. Nvidia based their own revenue guidance on the same assumptions about crypto mining that market analysts made. They missed their guidance in November because of that.

Nelson69 8y ago

Not just is it intact, but is there really an alternative ML/AI platform out there? They're still the best (only?) game in town with the full stack. And you can go buy or lease access to their parts right now and get radical performance improvements for your model training.

LAMY2000_EF 8y ago
kseems like a lot of startups are trying to build things equivalent to TPUs. Also one can use FPGAs for inference(?) maybe also training? I think a lot of people see an opportunity to try to take a slice of the big compute market for ML/AI. who knows if these alternatives really are viable.... but google has a lot of smart people and building your own silicon seems like a great idea these days..so I think other people will attack their stack.
- sitkack 8y ago
  AI is a pretty tractable workload for GPUs/TPUs/FPGAs. Inference is moving to many more places, the days of Nvidia being the solution for AI are over. It will still be the goto for research, but as things mature, the nets will literally run anywhere there is a MAC (multiply accumulate).
  - pmoriarty 8y ago
    It may run, but will it run as cost-effectively for the same amount of performance as it would on a GPU?
    - Setepenre 8y ago
      yeah, you have GPU like chips being developed by a lot of companies invested in AI. From server chips for training, down to cell phone chips for cheap inference. I am really hoping to see AMD entering the battlefield. AMD GPUs are more than capable of handling the workload it is just a software problem.