MitchJi
Trying to learn kindness, patience & forgiveness
A article linked to by Andrew Karpathy on twitter of particular interest to investors in Nvidia (@AlMc):
Deep Learning Hardware Limbo - Tim Dettmers
Deep Learning Hardware Limbo
2017-12-21 by Tim Dettmers 61 Comments
With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. So for consumers, I cannot recommend buying any hardware right now. The most prudent choice is to wait until the hardware limbo passes. This might take as little as 3 months or as long as 9 months. So why did we enter deep learning hardware limbo just now?
NVIDIA has decided that it needs to cash-in on its monopoly position before the competition emerges. It needs the cash in order to defend itself in the next 1-2 years. This is reflected by the choice to price the Titan V at $3000. With TensorCores the Titan V has a new shiny deep learning feature, but at the same time, its cost/performance ratio is abysmal. This makes the Titan V very unattractive. But because there is no alternative, people will need to eat what there are served – at least for now.
The competition is strong. We have AMD whose hardware is now already better than NVIDIA’s and plans to get itself together to produce some deep learning software which is actually usable. With this step, the cost/performance ratio will easily outmatch NVIDIA cards and AMD will become the new standard. NVIDIA’s cash advantage will help fight AMD off so that we might see very cheap NVIDIA cards in the future. Note that this will only happen if AMD is able to push forward with good software — if AMD falters, NVIDIA cards will remain expensive and AMD will have lost its opportunity to grab the throne.
There is also a new contender in town: The Neural Network Processor (NNP) form Intel Nervana. With several unique features, it packs quite a punch. These new features make me drool — they are exactly what I want as a CUDA developer. The NNP solves most problems I face when I want to write CUDA kernels which are optimized for deep learning. This chip is the first true deep learning chip.
In general, for a 1-chip vs 1-chip ranking, we will see Nervana > AMD > NVIDIA, just because NVIDIA has to service gaming/deep learning/high-performance computing at once, while AMD only needs to service gaming/deep learning, whereas Nervana can just concentrate on deep learning – a huge advantage. The more concentrated a designed architecture, the less junk is on the chip for deep learning.
However, the winner is not determined by pure performance, and not even by pure cost/performance. It is determined by cost/performance + community + deep learning frameworks.
Let’s have a closer look at the individual positions of Nervana, AMD, and NVIDIA to see where they stand.
<Snip>...<snip>
I’m confident that Tesla’s chip will be at least as good as intel’s chip and Tesla will be able to buy them for less. I wonder how long it will be before the Tesla chips are installed in alien dreadnaught robots.
Deep Learning Hardware Limbo - Tim Dettmers
Deep Learning Hardware Limbo
2017-12-21 by Tim Dettmers 61 Comments
With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. So for consumers, I cannot recommend buying any hardware right now. The most prudent choice is to wait until the hardware limbo passes. This might take as little as 3 months or as long as 9 months. So why did we enter deep learning hardware limbo just now?
NVIDIA has decided that it needs to cash-in on its monopoly position before the competition emerges. It needs the cash in order to defend itself in the next 1-2 years. This is reflected by the choice to price the Titan V at $3000. With TensorCores the Titan V has a new shiny deep learning feature, but at the same time, its cost/performance ratio is abysmal. This makes the Titan V very unattractive. But because there is no alternative, people will need to eat what there are served – at least for now.
The competition is strong. We have AMD whose hardware is now already better than NVIDIA’s and plans to get itself together to produce some deep learning software which is actually usable. With this step, the cost/performance ratio will easily outmatch NVIDIA cards and AMD will become the new standard. NVIDIA’s cash advantage will help fight AMD off so that we might see very cheap NVIDIA cards in the future. Note that this will only happen if AMD is able to push forward with good software — if AMD falters, NVIDIA cards will remain expensive and AMD will have lost its opportunity to grab the throne.
There is also a new contender in town: The Neural Network Processor (NNP) form Intel Nervana. With several unique features, it packs quite a punch. These new features make me drool — they are exactly what I want as a CUDA developer. The NNP solves most problems I face when I want to write CUDA kernels which are optimized for deep learning. This chip is the first true deep learning chip.
In general, for a 1-chip vs 1-chip ranking, we will see Nervana > AMD > NVIDIA, just because NVIDIA has to service gaming/deep learning/high-performance computing at once, while AMD only needs to service gaming/deep learning, whereas Nervana can just concentrate on deep learning – a huge advantage. The more concentrated a designed architecture, the less junk is on the chip for deep learning.
However, the winner is not determined by pure performance, and not even by pure cost/performance. It is determined by cost/performance + community + deep learning frameworks.
Let’s have a closer look at the individual positions of Nervana, AMD, and NVIDIA to see where they stand.
<Snip>...<snip>
I’m confident that Tesla’s chip will be at least as good as intel’s chip and Tesla will be able to buy them for less. I wonder how long it will be before the Tesla chips are installed in alien dreadnaught robots.
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