I do a startup called Lepton AI. We provide AI PaaS and fast AI runtimes as a service, so we keep a close eye on the IaaS supply chain. For the last few months we see supply chain getting better and better, so the business model that worked 6 months ago - "we have gpus, come buy barebone servers" no longer work. However, a bigger problem emerges. Probably a problem that could shake the industry: people don't know how to efficiently use these machines.
There are clusters of GPUs sitting idle because companies don't know how to use them. It's embarrassing to resell them too because that makes the images look bad to VCs, but secondary market is slowly happening.
Essentially, people want a PaaS or SaaS on top of the barebone machines.
For example, for the last couple months we were helping a customer to fully utilize their hundreds-of-card cluster. Their IaaS provider was new to the field. So we literally helped both sides to (1) understand infiniband and nccl and training code and stuff; (2) figure out control plane traffic; (3) built accelerated storage layer for training; (4) all kinds of subtle signals that needs attention. Do you know that a GPU can appear OK in nvidia-smi, but still encounter issues when you actually run a cuda or nccl kernel? That needs care. (5) fast software runtimes, like LLM runtime, finetuning script, and many others.
So I think AI PaaS and SaaS is going to be a very valuable (and big) market, after people come out of the frenzy of "grabbing gpus" - and now we need to use them efficiently.
Hi folks - Yangqing from Lepton here. The idea came from a coffee chat with a colleague on the question: how much of the RAG quality comes from the old good search engine, vs LLMs? And we figured out that the best way is to build a quick experiment and try it out. What we learned is that search engine results matter a lot, and probably more important than LLMs. We decided to put it up as a site and also open source the full code.
You can try plug in different search engines or even your own elastic interface, write different LLM prompts, pick different LLM models - a lot of ablation studies that could be tried out.
General availability of the structured decoding capability for ALL open-source models hosted on Lepton AI. Simply provide the schema you want the LLM to produce, and all our model APIs will automatically produce outputs following the schema. In addition, you can host your own LLMs with structured decoding capability without having to finetune
Thanks - we definitely agree that llama.cpp is great. Big fan of their optimizations. We are more or less orthogonal to the engines though - in the sense that we serve as the infra/platform to run and manage those implementations easily. For example, we support running a wider range of models - for example sdxl is one single line too:
lep photon run -n sdxl -m hf:stabilityai/stable-diffusion-xl-base-1.0 --local
It's really about how to productize a wide range of models as easy as possible.
In theory one can have 640G = 8 * 80G A100s memory and launch it. 180B Falcon with fp16 will be 360G, so there would be enough memory. It's definitely going to be very expensive indeed.
It's not only about "building a docker" but also maintaining multiple models, multiple environments and a lot of users. Imagine there is a group of engineers each needing to deploy their own models: one needs tensorflow 1.x, one needs tensorflow 2.x, one needs pytorch and one needs a very strange combination of dependencies. Trust me, things get complex very easily:
I definitely agree that for a fixed use case, building a docker once and for all is probably the simplest and best approach. However, it quickly gets more complex and out of hand.
Also the basic plan is free for independent developers. You don't need to pay more than as if you were using EC2 instances, but with the platform convenience - we definitely hope it's worth it!
To show some actual coding examples, We have made the python library open-source at https://github.com/leptonai/leptonai/. With it, launching a common HuggingFace model is as simple as a one liner. For example, if you have a GPU, Stable Diffusion XL is as simple as:
pip install -U leptonai
lep photon run -n sdxl -m hf:stabilityai/stable-diffusion-xl-base-1.0 --local
And you have a local OpenAPI server that runs it! Go to http://0.0.0.0:8080/docs, or use your favorite OpenAPI client.
We've been building AI API services using such tools ourselves. The easiest way to try out Lepton is to head to https://lepton.ai/playground and use our API service for popular models: Stable Diffusion, LLaMA, WhisperX, and other interesting showcases
We are proud of our performance. For example, we have probably the fastest LLaMA 7B and 70B model APIs, and it costs $0.8 to run 1 million tokens inference - we believe it's the most affordable one in the market. In addition, during the open beta phase, calling these services is free when you sign up for the Lepton AI platform.
Under the hood, we wrote a platform to allow you to run things easily on the cloud with ease. For example, if you find Pygmalion to be a great conversation model but you don't have a GPU, use lepton's Remote() capability to launch a service:
Wait a few minutes for the model to be downloaded and run, and you can now use it as if it were a standard python function:
print(pygmalion.run(inputs="Once upon a time", max_new_tokens=128))
If you are interested in the operational details, you can find fine-grained controls at https://dashboard.lepton.ai/ as a fully managed platform - we also support BYOC (bring your own compute) if you are an enterprise needing more autonomy over infrastructure.
I don’t want to be mean, but since you mentioned RCNN - no, you are dead wrong. RCNN was open sourced in 2014, check the repo: https://github.com/rbgirshick/rcnn
Not to mention that nvidia has thrown numerous open source efforts over the years. If SR was under the impression that 2017 was a dry year for open source deep learning vision systems - I can understand why it didn’t do very well technology wise.
Disclaimer: have been doing deep learning open source and research over the years. Have touched all major frameworks in the market.
Just to clarify a little bit... "At the time, very few object detection models had public implementations" - this is wrong. Almost all object detection models had public implementations starting from 2014, most notably Detectron (Caffe), GoogleNet/SSD (Tensorflow and matlab). Post 2015 when TensorFlow was released, one can find even more implementations.
Data is the problem. Everyone has the algorithm but not enough people have data (especially labeled ones)
So what we do is to keep syntax=proto2, but allow users to compile with both protobuf 2.x and protobuf 3.x libraries. Minumum need is 2.6.1. We kind of feel that this gives maximum flexibility for people who have already chosen a protobuf library version.
There are clusters of GPUs sitting idle because companies don't know how to use them. It's embarrassing to resell them too because that makes the images look bad to VCs, but secondary market is slowly happening.
Essentially, people want a PaaS or SaaS on top of the barebone machines.
For example, for the last couple months we were helping a customer to fully utilize their hundreds-of-card cluster. Their IaaS provider was new to the field. So we literally helped both sides to (1) understand infiniband and nccl and training code and stuff; (2) figure out control plane traffic; (3) built accelerated storage layer for training; (4) all kinds of subtle signals that needs attention. Do you know that a GPU can appear OK in nvidia-smi, but still encounter issues when you actually run a cuda or nccl kernel? That needs care. (5) fast software runtimes, like LLM runtime, finetuning script, and many others.
So I think AI PaaS and SaaS is going to be a very valuable (and big) market, after people come out of the frenzy of "grabbing gpus" - and now we need to use them efficiently.