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.
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.
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.
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.
In this one we were only using 3.1 405B FP8. We took one model to simplify the setup and were mostly looking at the memory saturation effect. So basically we compared inference metrics of the same model. I suppose comparing 3.1 and 3.2 will be difficult as they are different models entirely. But open to ideas
Yes, we should have included the price too—thanks for pointing that out. I forgot about it. Appreciate you adding the links here! And yes, Lambda's and Hot Aisle's prices were used in the calculation.
> What are the differences in opinions between dstack and SkyPilot?
SkyPilot is great. I think there are many tiny details though. At dstack, we try to provide out-of-the-box and more high-level experience.
Examples:
1. Authorization built-into services
2. Dev environments with IDE integration
3. HTTPS out of the box with an ability to set up own domains
4. Projects for team management and resource isolation
5. Hardware metrics tracking
Also, we try to distance from Kubernetes and improve our own orchestrator that natively integrates with cloud providers
> Same question could be posed to Modal.
Modal is great too. Modal's strengths is Python decorators and their focus on cloldstarts/serverless kind of experience.
dstack here is more about flexibility/multi-cloud/on-prem/etc.
For example, I personally prefer being able to run any code with dstack without changing my code. Otehr people may prefer Python decorators.
> Aren’t you worried about the amount of product development that goes into Kubernetes and its ecosystem?
A very good question. I think its both a strength and a weakness of Kubernetes. So far we see that for us it's a lot easier to bring AI-native experience, simplify it, and make it more out of the box.
We of course respect K8S though. But we think the community to deserve options!
> What about democratic-csi?
Haven't seen it yet. Will look into it.
At dstack, we support volumes for both cloud and on-prem.
> NVIDIA GPU Operator, AMD’s k8s device plugins
We aim to support any accelerators out of the box.
I guess it depends on the use case. For example with dstack, we focus on AI.
Our abstractions include:
1. Dev environments - you need them often and need an easy way to get one with tight GOU resources - either using already provisioned resources or provision on-demand
2. Tasks. For example, in AI you may want to run distributed tasks over a cluster using your favorite framework like pytroch
3. Services - very close to Docker Compose. And you can use it with dstack. But for you may want to also manage GPU requirements; and of course auto-scaling
4. Managing clusters. As an AI user you may want to provision them on-demand. This is what we call fleets with dstack.
5. Ingress for public endpoints. Dstack also handles authorization and OpenAI endpoint mapping - as it’s important for AI.
6. Finally you need to manage tenancies - isolate resources across projects or teams. With dstack, we call it projects.
Oh, excited to see dstack featured here. Founder and core contributor to dstack here. Yes, we aim to simplify container orchestration for AI and build an alternative to both K8S and Slurm - for both multi-cloud and on-prem.
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