101–110 of 117 posts
Earlier quoted context omitted.
I am increasingly growing tired of these "cloud" services, paid or not. :/
Well, feel free to spend your own time on writing such a tool and releasing it as Open Source. That would be a really cool project! Until then, don't complain that others aren't willing to donate a significant amount of their work to the public.
Earlier quoted context omitted.
I am increasingly growing tired of these "cloud" services, paid or not. :/
Well, feel free to spend your own time on writing such a tool and releasing it as Open Source. That would be a really cool project! Until then, don't complain that others aren't willing to donate a significant amount of their work to the public.
Earlier quoted context omitted.
I am increasingly growing tired of these "cloud" services, paid or not. :/
Well, feel free to spend your own time on writing such a tool and releasing it as Open Source. That would be a really cool project! Until then, don't complain that others aren't willing to donate a significant amount of their work to the public.
Earlier quoted context omitted.
That understanding of the system is correct. To make it practical we've implemented a bunch of optimizations to minimize I/O cost. You can see how it performs on inference with BERT here: https://youtu.be/qsOBFQZtsFM?t=69 . The overheads are larger for training compared to inference, and we are implementing more optimizations to approach native performance.
Is DirectX support possible any time soon? This would be huge for Windows VMs on Linux...
Given the interest here we decided to open up T4 instances for free. Would love for y'all to try it and let us know your thoughts!
What is your A100 and H100 pricing?
Earlier quoted context omitted.
Is DirectX support possible any time soon? This would be huge for Windows VMs on Linux...
You could use unofficial Windows drivers for virtio-gpu, that are specifically intended for VM use.
Earlier quoted context omitted.
Exactly! The finer grain sharing is one of the key things on our radar right now
www.juicelabs.co does all this today, including the GPU sharing and fractionalization.
Earlier quoted context omitted.
That understanding of the system is correct. To make it practical we've implemented a bunch of optimizations to minimize I/O cost. You can see how it performs on inference with BERT here: https://youtu.be/qsOBFQZtsFM?t=69 . The overheads are larger for training compared to inference, and we are implementing more optimizations to approach native performance.
> to approach native performance. The same way one "approaches the sun" when they take the stairs?
Earlier quoted context omitted.
How do you use it for video encoding/decoding? Won't the uncompressed video (input for encoding or output of decoding) be too large to transmit over network practically?
Well, the ffmpeg-over-ip tool in the GP does it by just not sending uncompressed video. It's more of an ffmpeg server where the server is implicitly expected to have access to a GPU that the client doesn't have, and only compressed video is being sent back and forth in the form of video streams that would normally be the input and output of ffmpeg. It's not a generic GPU server that tries to push a whole PCI bus over…