This is great news. It’s extremely important to the research community that large companies enter into the DL silicon space to contend with NVIDIA’s monopoly.
NVIDIA is now exerting pricing power to the point where they’ve decided to train their sales people to disregard the metric that is most important to customers: cost of training. Talk with one of their enterprise sales people and you’ll find they’ll say things like “FLOPS / $ doesn’t matter” to justify a 10x increase in price for their TESLA line. As history has shown, a monopolist sows the seeds of their own destruction and, by disregarding the metrics that matter, they alienate their customers.
Here are open source projects that you can contribute to to break the monopoly:
Choice in the marketplace is still choice. And, Google might eventually decide to sell this to outside companies. Eventually the desire to build competitive advantages for Google Cloud might be surpassed by the desire to compete with NVIDIA. Commoditizing the complement is a powerful strategy.
We're working to provide more choice with PlaidML as well. You can use it to run Keras, ONNX, and TF models on AMD, Intel, and mobile/embedded GPUs, on most operating systems and OpenCL, LLVM, OpenGL, and soon CUDA and Metal, etc. We would welcome contributions for TensorFlow integration, ROCm support, and anything else that widens support to more people.
A couple people internally have looked at Vulkan, it seems like it should work really well but we're not actively working on it. You can get an idea of how Vulkan support would look by looking at the existing HALs:
TPUs seem like great hardware. Unfortunately google's insistence on keeping them in a walled garden is a major deterrent to building something that relies on their platform.
You seem to be missing the point again and again every time this topic comes up. There is a reason why TensorFlow is generic enough to run on many types of hardware. That allows people to port their code around. But there exists a world outside of tensorflow that you might not be aware of, and for that world this chip is not a competitor. That world is significantly larger than the TF world.
Well until that actually happens I will be buying more NVIDIA stock.
I have done deep learning also and I don't see NVIDIA being an incumbent that is easy to be knocked out. I think we would need some distruption such as a new technique that is more performant or different design goals. Intel is still leading the way and I don't see NVIDIA being unseated with its head start anytime soon.
I'm not as pessimistic. 3dfx and Matrox didn't look like easy to knock out opponents in the 90s and yet here we are. Markets are powerful incentive mechanisms.
"Slowly grinds the mill of the gods, but it grinds fine."
Costs to compete in the silicon business have inflated massively since that era, its perhaps 100 or even 1000 times more expensive to build stuff like fabs (although NVIDIA dosent own fabs), and design chips.
>3dfx and Matrox didn't look like easy to knock out opponents in the 90s and yet here we are.
Matrox didn't give much of a damn to 3D gaming, 3Dfx were bust doing all the wrong things. It was certainly not "easy", but don't seems hard or impossible either at the time.
Written in Nim, which looks and feels a lot like Python, but runs a lot like C++ or Rust, especially in the speed and FFI departments, and deploys like Go as a small single fast executable. Already supports CPU, CUDA and OpenCL. Still early, but with a lot of promise. Use is modeled on some mixture of PyTorch and Keras.
Self-plug: Gorgonia[0] can support OpenCL as well (but time and effort is required to write support libs). Would appreciate contributions. I recently gave a talk on how to interface with foreign libs[1]. Writeup soon to come.
Water cooling on their TPU is probably because they cost way more to buy than the extra cost of special cooling. It is also probably to increase “spec” on their new version without the best improvements in hardware/silicon.
NVIDIA is now exerting pricing power to the point where they’ve decided to train their sales people to disregard the metric that is most important to customers: cost of training. Talk with one of their enterprise sales people and you’ll find they’ll say things like “FLOPS / $ doesn’t matter” to justify a 10x increase in price for their TESLA line. As history has shown, a monopolist sows the seeds of their own destruction and, by disregarding the metrics that matter, they alienate their customers.
Here are open source projects that you can contribute to to break the monopoly:
ROCm: https://rocm.github.io/
MIOpen: https://github.com/ROCmSoftwarePlatform/MIOpen
TensorFlow: https://github.com/tensorflow/tensorflow