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Show HN: GPU-Accelerated Inference Hosting

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Re: Show HN: GPU-Accelerated Inference Hosting

#31

Looking at the examples in the landing page.. so I don't need any kind of authentication to do inference? Anyone can run the models I upload?

At the moment, no, only the random hash gives some kind of security by obfuscation. More advanced security controls are coming soon.

Re: Show HN: GPU-Accelerated Inference Hosting

#32
post #29

Or, do your inference using an AVX-512 CPU: https://NN-512.com (open source, free software, no dependencies) With batch size 1, NN-512 is easily 2x faster than TensorFlow and does 27 ResNet50 inferences per second on a c5.xlarge instance. For more unusual networks, like DenseNet or ResNeXt, the performance gap is wider. Even if you allow TensorFlow to use a larger ResNet50 batch size, NN-512 is easily 1.3x faster. If…

Oh that's very interesting, how ready for production is it? It only works for TF right? > If you need a few dozen inferences per second per server, this is the cheapest way. And you're not depending on a proprietary solution whose parent company could go out of business in a year. Definitely the cheapest way. We've been in business for more than a year already actually :)

NN-512 has no connection to TensorFlow. It is an open source Go program (with no dependencies) that generates C code (with no dependencies). And it's fully ready for production. Similarly, LibNC is stand-alone, and Fabrice Bellard (author of FFmpeg, QEMU, etc.) will release the source to anyone who asks for it.

I'm giving performance comparisons versus TensorFlow, which I consider to be a standard tool.

People who use your proprietary, closed, black-box service are dependent on the well-being of your business. You could vanish tomorrow.

Re: Show HN: GPU-Accelerated Inference Hosting

#33

Earlier quoted context omitted.

Well, I guess I know where I am going to host GPT-J-6B then. I don't think it is sustainable.

How are you planning to put a gpt whatever when the service clearly have a model size limit?!

The size limit is very close to allowing it (12GB vs 10GB). I imagine you can reduce it somewhat further and get it to fit.

Re: Show HN: GPU-Accelerated Inference Hosting

#34

Looks awesome. Do you know if and how you guys support NVIDIA's software. For my project the NVIDIA software I'm using states it needs: CUDA 11.3.0 cuBLAS 11.5.1.101 cuDNN 8.2.0.41 NCCL 2.9.6 TensorRT 7.2.3.4 Triton Inference Server 2.9.0 I'm new to deploying to production inference so I'm not sure if those are easily portable across such platforms or not really.

Those frameworks are installed by default in our custom environment. There is no additional setup/configuration required from you.

Re: Show HN: GPU-Accelerated Inference Hosting

#35
post #31

Looking at the examples in the landing page.. so I don't need any kind of authentication to do inference? Anyone can run the models I upload?

At the moment, no, only the random hash gives some kind of security by obfuscation. More advanced security controls are coming soon.

Also, if someone uses my model, do I pay for it?

Re: Show HN: GPU-Accelerated Inference Hosting

#36
post #31

Looking at the examples in the landing page.. so I don't need any kind of authentication to do inference? Anyone can run the models I upload?

At the moment, no, only the random hash gives some kind of security by obfuscation. More advanced security controls are coming soon.

Anyway, here's a must: having a different key for uploading a model and doing inference with it. Or even, there should be a set of keys for each model, with each access logged separately.
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