Our approach though is more tide-case agnostic and in the direction of brining full-fledged container orchestration converting from development to training and inference
It’s so ridiculous to see TPUs being compared to NVIDIA GPUs. IMO proprietary chips such as TPU had no future sure to the monopoly on the cloud services. There is no competition across the cloud services providers. The only way to access TPUs is through GCP.
As the result nobody wants to use them regardless of the technology. This is the biggest fault of GCP. Further the road, the gap between NVIDIA GPUs and Google TPUs (call it „moat“ or CUDA) is going to grow.
The opposite situation is with AMD which are avoiding the mistakes of Google.
My hope though is that AMD doesn’t start to compete with cloud service providers, e.g. by introducing their own cloud.
One thing worth note here - TPUs are optimized for a fairly constrained set of operations. Google’s had good success with them, but, like many of the other Google architectural choices, this will constrain Google’s technical choice space in the future - if they’ve gone all in on TPUs, future Google machine learning projects will be using the sets of operations the TPUs excel at because that’s what Google has a lot of, not necessarily because that’s the optimal choice. This will have knock-on effects across the industry due to Google’s significant influence on industry practice and technical direction.
Every major Cloud vendor is trying to develop their custom AI ASIC. Putting Google aside, Amazon has trainium/inferentia, which Anthropic uses quite extensively. Microsoft is doing sth. similar, although they are quite behind. OpenAI is doing it. Meta is doing it. That's why the stock price of Broadcom/Marvell soared.
Thank you! If you sign up for the waitlist on the aibutton.io landing page, I'll send you an email with answers to your questions once I've set up a product page and start selling.
I expect the product to be available in mid-August. A demo, along with information on price, battery life, etc., will be available in mid-July. I'll then email it to everyone on the waitlist.
I think it’s not misleading, but rather very clear that there are problems. v7 is compared to v5e. Also, notice that it’s not compared to competitors, and the price isn’t mentioned.
Finally, I think the much bigger issue with TPU is the software and developer experience. Without improvements there, there’s close to zero chance that anyone besides a few companies will use TPU. It’s barely viable if the trend continues.
> besides a few companies will use TPU. It’s barely viable if the trend continues
That doesn't matter much of those few companies are the biggest companies. Even with Nvidia majority of the revenue is being generated by a handful of hyperscalers.
You said: "notice that it’s not compared to competitors"
The article says: "When scaled to 9,216 chips per pod for a total of 42.5 Exaflops, Ironwood supports more than 24x the compute power of the world’s largest supercomputer – El Capitan – which offers just 1.7 Exaflops per pod."
I believe my original sentence was accurate. I was expecting the article to provide an objective comparison between TPUs and their main competitors. If you’re suggesting that El Capitan is the primary competitor, I’m not sure I agree, but I appreciate the perspective. Perhaps I was looking for other competitors, which is why I didn’t really pay attention to El Capitan.
Hey HN, founder of dstack here. We've been working on this over three months and pretty excited about this release.
Basically, the main point is that dstack is an open-source AI-native alternative to Kubernetes, designed to be more lightweight, and focusing just on AI workloads on both cloud and data-centers.
With this release we are adding the critical feature that allows to run containers concurrently on same host slicing its resources incl. GPU for a more cost-efficient utilization.
Another new thing is the simplified way to run things on private clouds where clusters are often behind a login node.
There are many more cool things on our roadmap to ensure dstack is a streamlined alternative to both K8S and Slurm. Our roadmap can be found in [1]
Super excited to hear any feedback.
We recently also added support for agents: https://skills.sh/dstackai/dstack/dstack
Our approach though is more tide-case agnostic and in the direction of brining full-fledged container orchestration converting from development to training and inference