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Main difference is that we are more opinionated (in terms of configurations) and sort of do the scrolling and sorting out for you – hopefully a bit smoother as
by nicowaltz 2y ago
Main difference is that we are more opinionated (in terms of configurations) and sort of do the scrolling and sorting out for you – hopefully a bit smoother as a user experience. We sort out bad machines immediately. We're also directly working on making compute from unknown high-end data centers available, there's a lot of unused compute out there! See gpulist.ai
Also, don't know if vast.ai does this, but with us you can have 6 user sessions on your machine if you have six GPUs, so granular utilization is possible.
- bradfox2 2y agoWe own and operate 40+ data center GPUs (v100s, a100s, and ax000s) in a private cluster and use vast to rent unused capacity. What would make you better than vast is extremely easy spot leasing and job prioritization. I want to be able to have one of our training jobs finish, and then have the capacity immediately transition to a lease. With vast, we are renting in week long blocks.
- nicowaltz 2y agoExactly, that's the idea
- bradfox2 2y agoIs it implemented?
- bigcat12345678 2y agoYou can do that on llm.sxwl.ai Shoot me an email at z@sxwl.ai for instructions, the web site is pretty outdated, the main UI is through restful API (which we don't have time to write doc yet)
- mkl 2y agoIf you have time to answer emails with instructions, you have time to update your site and documentation. Why not just do that?
- grepfru_it 2y agoDon’t build something if you don’t have a use case. All you have is a wishlist, until someone says “yes I want this here is $$$” which I assume the email will facilitate.
- DeathArrow 2y agoCan you expand on spot leasing and job prioritization? What kind of api would you prefer? How would you like to adjust time slices?
- jcannell 2y agoYou should be able to do that right now on vast. You just need to rent the gpus yourself with your own on demand instance(s) for your training job. As soon as it finished you then stop or destroy those instance(s) and the GPUs are available immediately (and if there are any other instances queued up in scheduling they will start up). Your actual job doesn't necessarily need to run in the container (if you know what you are doing). (I'm the founder of vast btw - contact us for help on setting this up and/or any feedback on making it an easier/better process)
- cfn 2y agoJust a quick comment: The country list in your Add Basic Node Data is not sorted.
- nicowaltz 2y agoWill fix.