3 ms·

Intel seems to be taking a long time getting things to market in terms of SKUs that the general developer can buy. This is critical for gaining mindshare for th

by dman 10y ago

Intel seems to be taking a long time getting things to market in terms of SKUs that the general developer can buy. This is critical for gaining mindshare for the programming model. Both Knights Landing and the new chips from Altera have failed to materialise into general availability in a SKU priced at <500$. Part of what has made Nvidia so successful here is that the CUDA ecosystem scales up and down their entire product line.

moyta 10y ago
That and getting mindshare in the academic compute space. Where Supermicro is cleaning out all of Nvidia's higher end SKUs to build these academic compute clusters, Intel needs to insert itself and try to take the market. Currently they pair a mid-range Xeon with a couple GPUs, Intel needs to change that model if they want to win the market that has kept Nvidia in business.
zamalek 10y ago
> the general developer can buy Having a non-integrated GPU (i.e. NVIDIA or AMD) in a machine is not a stretch of the imagination these days. This means that any curious dabbler can grab Tensorflow and start learning ML. Over the past few months, I've been seeing exactly this on HN: many developers with ML Show HNs. In my opinion, it is precisely the general availability of ML that has caused this explosion. As you pointed out, the above scenario falls away entirely under an expensive SKU. I strongly believe that having to buy any specific SKU in order to approach this stuff will spell doom for the tech. If it's not on every SKU down to the Core 2, I really can't see it taking off.
- petra 10y ago
  Why would na expensive SKU be a problem ? as long as they offered it as a service and they ROI would be significantly better, it would be fine.
  - nl 10y ago
    Because library developers want to have things available without paying by the hour. Once you have the software, a service is fine. But the ecosystem of tools around NVidia CUDA and CuDNN is there because you can buy a gaming card for a few hundred dollars and hack up libraries you find useful.
  - dman 10y ago
    Its significantly easier in academia to teach courses about hardware that is ubiquitous.

by dman 10y ago

moyta 10y ago

That and getting mindshare in the academic compute space. Where Supermicro is cleaning out all of Nvidia's higher end SKUs to build these academic compute clusters, Intel needs to insert itself and try to take the market. Currently they pair a mid-range Xeon with a couple GPUs, Intel needs to change that model if they want to win the market that has kept Nvidia in business.

zamalek 10y ago

> the general developer can buy Having a non-integrated GPU (i.e. NVIDIA or AMD) in a machine is not a stretch of the imagination these days. This means that any curious dabbler can grab Tensorflow and start learning ML. Over the past few months, I've been seeing exactly this on HN: many developers with ML Show HNs. In my opinion, it is precisely the general availability of ML that has caused this explosion. As you pointed out, the above scenario falls away entirely under an expensive SKU. I strongly believe that having to buy any specific SKU in order to approach this stuff will spell doom for the tech. If it's not on every SKU down to the Core 2, I really can't see it taking off.

petra 10y ago
Why would na expensive SKU be a problem ? as long as they offered it as a service and they ROI would be significantly better, it would be fine.
- nl 10y ago
  Because library developers want to have things available without paying by the hour. Once you have the software, a service is fine. But the ecosystem of tools around NVidia CUDA and CuDNN is there because you can buy a gaming card for a few hundred dollars and hack up libraries you find useful.
- dman 10y ago
  Its significantly easier in academia to teach courses about hardware that is ubiquitous.